Patent Publication Number: US-2022220709-A1

Title: Construction machinery with learning function

Description:
TECHNICAL FIELD 
     The present disclosure relates to construction machinery with learning function. 
     BACKGROUND ART 
     Construction machinery disclosed in Patent Document 1 is known as one example of construction machinery having learning function. This construction machinery learns a target value of an overlapping length of roll-compacted paths in a width direction. 
     REFERENCE DOCUMENT OF CONVENTIONAL ART 
     Patent Document 
     
         
         [Patent Document 1] JP6360583B1 
       
    
     DESCRIPTION OF THE DISCLOSURE 
     Problems to be Solved by the Disclosure 
     The construction machinery described above cannot learn a work performed by the construction machinery through manipulation by a human, and automatically perform the learned work. 
     Moreover, when the construction machinery learns the work performed through the manipulation by the human, and a general learning method is simply applied, the learning needs to be achieved at the highest level such that a quality of the work performed by the construction machinery is maintained at the same level as a work manipulated by a highly-skilled operator. Furthermore, the operation of the construction machinery needs to be changed according to modes of the work. However, since variations of work modes are innumerous, enormous amount of learning data and startup period of time for learning are required in order to appropriately control the operation of the construction machinery according to the actual mode of the work. Therefore, it is difficult to achieve the automation of the work performed by the construction machinery within a short period of time. 
     Moreover, in the construction industry, since the workforce is aging and running short due to the low birthrate and aging population, handing down the skills of the highly-skilled operators is urgent. However, the number of successors is also declining. 
     The present disclosure is made in order to solve the problems described above, and the first purpose thereof is to provide construction machinery, capable of learning a work performed by the construction machinery through manipulation by a human, and automatically performing the learned work. 
     In addition to the first purpose, the second purpose of the present disclosure is to provide skill-inheriting construction machinery, capable of taking over (inheriting) skill of a highly-skilled operator in the construction industry, and achieving automation of a given work within a short period of time. 
     SUMMARY OF THE DISCLOSURE 
     The present inventors carried out diligent examination in order to solve the above problems, and obtained the following findings as a result. 
     In construction machinery, an operating part including a working part is driven by a plurality of hydraulic actuators. For example, considering a hydraulic excavator as one example, a boom, an arm, a bucket, and a driver&#39;s seat correspond to the operating part, and hydraulic cylinders which rotate the boom, the arm, and the bucket, and a hydraulic motor which rotates the driver&#39;s seat correspond to the hydraulic actuators. 
     Therefore, a manipulating part is comprised of a plurality of manipulation levers corresponding to the number of the plurality of hydraulic actuators, and an operator carries out a work while operating the plurality of manipulation levers. Moreover, the construction machinery performs various works. Therefore, conventionally, it is considered to be difficult to cause the construction machinery to learn the work. 
     However, the present inventors paid attention to existence of comparatively simple routine work in the construction work. Learning of such a comparatively simple routine work is comparatively easy. 
     Meanwhile, in a certain device, in order to carry out the learning of the work performed through the manipulation by the operator, it is important to identify information which significantly influences the manipulation by the operator, and to use it as input data during the learning. 
     In this respect, the present inventors analyzed manipulation by highly-skilled operators, and obtained the following findings as a result. 
     To put it simply, they found out that the operator visually confirms a state of a work, perceives the current state of operation by construction machinery through visual confirmation and operation of manipulation levers, and senses a reaction from a work-target object, in order to determine the next manipulation. 
     Considering a digging work performed by a hydraulic excavator as one example, the operator visually confirms a state of the digging, and intuits how a bucket, a boom, and a driver&#39;s seat are currently to be operated, through visual confirmation of postures of the bucket and the boom, and through the current manipulated positions of manipulation levers. Moreover, when the bucket acts on the ground (digs the ground, rakes earth and sand, etc.), the operator determines, by sensing the reaction, whether or not an intended work (action) is performed. Then, the operator determines the next manipulation instantly considering these. Here, the reaction is, for example, an inclination, acceleration, and angular acceleration at the driver&#39;s seat. Moreover, it also became apparent that the operator puts importance on a speed, noise, etc. of an engine which is a driving source, when he/she determines the next manipulation. 
     Here, the state of the digging is one example of information indicating the work state. The manipulated position is one example of information indicating the operation state of the hydraulic excavator. The inclination of the driver&#39;s seat etc. is one example of reaction from the ground. The noise etc. of the engine is information indicating a state of the driving source, and thus, indicating the operation state of the hydraulic excavator. 
     Therefore, the present inventors thought to use, as the input data (estimation basic data) during the learning, at least data indicative of the work state, data indicative of the operation state of the operating part, and data indicative of the reaction received by the operating part from the work-target object due to the work performed by the working part. The present disclosure is made in view of such perspectives. 
     In order to achieve the purpose, construction machinery with learning function according to one aspect of the present disclosure includes an operating part having a working part and configured to move the working part to perform a work, a manipulating part configured to output a command corresponding to operation by an operator, a work-state detecting part configured to detect a state of the work performed by the working part and output the detected work state as work-state data, an operation-state detecting part configured to detect a state of the operation of the operating part and output the detected operation state as operation-state data, a reaction detecting part configured to detect a reaction received by the operating part from a work-target object due to the work performed by the working part, and output the detected reaction as reaction data, a learning data memory configured to store the command in a time series as command data, and store estimation basic data in a time series, the estimation basic data including the work-state data, the operation-state data, and the reaction data, a learning module configured to execute machine learning of the command data stored in the learning data memory by using the estimation basic data stored in the learning data memory, and, after the machine learning, receive an input of the estimation basic data during the operation of the operating part, and output an estimated command of the command, and a hydraulic drive system configured to drive the operating part based on one of the command and the estimated command, or both of the command and the estimated command. Here, the “command data” means data used as teacher data in case of “supervised learning,” the “estimation basic data” means data to be inputted to the learning module and used to cause the learning module to estimate an operation command, and the phrase “move the working part” means “cause the working part to operate and move.” 
     According to this configuration, the given work is carried out during the learning while a highly-skilled operator manipulates the construction machinery. Then, the command data corresponding to the command based on the manipulation, and the estimation basic data including the work-state data of the working part, the operation-state data of the operating part, and the data of the reaction received by the operating part from the work-target object due to the work performed by the working part, are stored in the learning data memory. Then, the learning module receives the input of the estimation basic data stored in the learning data memory so as to execute the machine learning of the learning data to output the estimated command. Therefore, the manipulation by the highly-skilled operator can be learned, and outputted as the estimated command. When the estimation basic data is inputted into the learning module after the machine learning is finished, the learning module outputs the estimated command. Then, the hydraulic drive system drives the operating part based on the estimated command. As a result, the manipulation by the highly-skilled operator is learned, and the work is automatically performed based on the estimated command which is outputted from the learning module as a result of the learning. 
     Therefore, the construction machinery can be provided, which is capable of learning the work performed by the construction machinery through the manipulation by the human, and automatically performing the learned work. 
     The manipulating part may be configured to output, as the command, an operation command corresponding to the operation by the operator. The learning data memory may be configured to store the operation command in a time series as the command data, and store the estimation basic data in a time series, the estimation basic data including the work-state data, the operation-state data, and the reaction data. The learning module may be configured to execute, during the learning, the machine learning of the command data stored in the learning data memory by using the estimation basic data stored in the learning data memory, and during an automatic control after the machine learning, receive the input of the estimation basic data and output an estimated operation command that is the estimated command. Further, the hydraulic drive system may be configured to drive the operating part according to the operation command or the estimated operation command. 
     According to this configuration, since the hydraulic drive system is configured to drive the operating part based on the estimated operation command that is the estimated command of the operation command, the work is automatically performed based on the result of the learning of the manipulation by the highly-skilled operator. 
     The construction machinery may further include a body part provided with the operating part. The reaction detecting part may detect the reaction including at least one of inclination, acceleration, and angular acceleration of the operating part or the body part, and output the detected reaction as the reaction data. 
     According to this configuration, the estimation basic data includes the inclination, the acceleration, or the angular acceleration of the operating part or the body part, on which the highly-skilled operator puts importance when he/she determines the next manipulation, and thus, the accuracy of the learning improves. Note that, since the operating part is provided to the body part, when the operating part receives the reaction from the work-target object, this reaction influences the body part. 
     The operation-state detecting part may detect a state of a driving source including at least one of an output and operation noise of the driving source, and output the detected driving-source state as driving-source state data, the driving source driving a pump configured to pump hydraulic oil of the hydraulic drive system. The operation-state data may include the driving-source state data. 
     According to this configuration, the estimation basic data includes the output or the noise of the driving source, on which the highly-skilled operator puts importance when he/she determines the next manipulation, and thus, the accuracy of the learning improves. 
     The operation-state data may include an operation command detected by an operation command detecting part. Here, the phrase “the operation-state data includes an operation command” means that, in case of the “supervised learning,” the operation command stored in the learning data memory in a time series is sequentially used as the teacher data, and the operation command at a time point “in a time series before” the operation command used as the teacher data, is used as the operation-state data. 
     According to this configuration, the estimation basic data includes the operation command corresponding to the current position of a manipulation lever (the manipulation part), on which the highly-skilled operator put importance when he/she determines the next manipulation, and thus, the accuracy of the learning improves. 
     The operation-state detecting part may further include a posture detecting part configured to detect a posture of the operating part and output the detected posture as postural data. The operation-state data may include the postural data. 
     According to this configuration, the estimation basic data includes the postural data of the operating part, on which the highly-skilled operator puts importance when he/she determines the next manipulation, and thus, the accuracy of the learning improves. 
     The construction machinery with learning function may be skill-inheriting construction machinery provided with a control part. The manipulating part may be configured to output, as the command, a manual operation correcting command according to operation by the operator. The hydraulic drive system may be configured to drive the operating part according to a basic operation command, an automatic operation correcting command, and the manual operation correcting command. The control part may include a basic-operation commanding module configured to output the basic operation command for causing the working part to take a basic movement by the operating part, an operation-correcting-command generating module configured to generate an operation correcting command by adding the manual operation correcting command to the automatic operation correcting command, an operation-correcting-command memory that is a command memory and configured to store the operation correcting command in a time series, an estimation-basic-data memory, and the learning module. The learning module may be configured to execute machine learning of the operation correcting command stored in the operation-correcting-command memory by using the estimation basic data stored in the estimation-basic-data memory, and, after the machine learning, receive the input of the estimation basic data during the operation of the operating part and output the automatic operation correcting command that is the estimated command. 
     According to this configuration, the operating part moves the working part, through the hydraulic drive system, according to the basic operation command, the automatic operation correcting command, and the manual operation correcting command. Therefore, when the operation of the operating part is not corrected by the operator and is not applied with the automatic operation correction by the learning module, the operating part causes the working part to take the basic movement according to the basic operation command outputted from the basic-operation commanding module. The operator monitors the motion of the working part while visually checking the work by the working part, and when the basic movement cannot achieve the given work in a highly-skilled manner, the given work is manually corrected to be performed in the highly-skilled manner. Then, the manual operation correcting command corresponding to this manual correction is outputted from the manipulating part to correct the basic movement, and thus, the given work is performed in the highly-skilled manner. 
     Meanwhile, the manual operation correcting command related to the given work is added to the automatic operation correcting command outputted from the learning module to generate the operation correcting command, and the machine learning of this operation correcting command is executed by the learning module. 
     When the learning module does not perform the automatic correction as described above, the learning module only learns the manual operation correcting command based on the manual operation correction by the operator. Since the learning module receives the input of the estimation basic data corresponding to the movement of the working part during the operation of the operating part, when an operating state in which the given work is not performed in the highly-skilled manner similarly to the case described above occurs, the operation correcting command estimated by the learning is outputted from the learning module as the automatic operation correcting command. Thus, the basic operation command is automatically corrected to the direction in which the given work is performed in the highly-skilled manner, and if this automatic correction is appropriate, the given operation can be performed in the highly-skilled manner. 
     However, if the learning is insufficient, or if the operation state of the operating part when the given work is not performed in the highly-skilled manner is greatly different from one estimated by the learning module, the given work is not performed in the highly-skilled manner even when the correction is performed. In this case, the operator manually correct the given work to be performed in the highly-skilled manner, thereby, the given work is performed by the operating part in the highly-skilled manner. Then, the manual operation correcting command corresponding to the additional manual operation correction is added to the automatic operation correcting command corresponding to the previous manual operation correction, and is learned by the learning module. 
     Therefore, the correcting ability of the learning module to the basic movement of the working part improves. After that, these operations are repeated, and when the correcting ability of the learning module to the basic movement of the working part improves to the equivalent level to that of the operator, the correction to the basic movement of the working part by the operator becomes unnecessary. In this state, the learning module appropriately corrects the basic movement of the working part instead of the operator, and the given work can be appropriately performed by the working part. 
     In this manner, when the operator is a highly-skilled operator, the manual operation correction by the operator constitutes “skill” of the highly-skilled operator, this “skill” is accumulated in the learning module and handed down to the learning module, and the learning module becomes a “successor” of the “skill” of the highly-skilled operator. As a result, the construction machinery with the learning module becomes “skill-inheriting construction machinery.” 
     Moreover, according to this configuration, the operating part is configured to operate, through the hydraulic drive system, according to the basic operation command, the automatic operation correcting command, and the manual operation correcting command. Therefore, when insufficient automatic operation correcting command is outputted from the learning module, the operator can perform the manual operation correction while watching the movement of the working part so as to cause the operating part to operate appropriately. Thus, appropriate trial and correction of the operation can be performed in the practice at the work site. In other words, since the learning module can learn through the practice at the work site, enormous amount of learning data and startup period of time for the learning module become unnecessary. As a result, the automation of the given work can be achieved in the short period of time. 
     Moreover, according to this configuration, since a part of the basic movement of the working part related to the given work, which is unnecessary to be corrected, is automatically executed by the basic-operation commanding module, the operator only performs the necessary correction. Therefore, a load for the operator is reduced. Moreover, since the work varies even by the highly-skilled operator, the accuracy of the work improves when only a part of the work is performed through the manipulation by the operator as described above, compared to the case where the entire work is performed through the manipulation by the operator. 
     Moreover, it can be considered that the skill of the highly-skilled operator is taken over by storing the manual operation correcting command corresponding to the manual operation correction by the operator in the memory. However, since infinite modes exist, in which the basic movement of the working part needs to be corrected, it is actually difficult to take over the skill of the highly-skilled operator in such a method. Meanwhile, by using the learning module as the configuration described above, the learning module learns the manual operation correction (accurately, the manual operation correcting command) according to the mode every time the correction of the basic movement of the working part is required, and thus, the taking over of the skill of the highly-skilled operator can be achieved easily. 
     Further, according to this configuration, during the operation of the operating part, the highly-skilled operator manipulates the construction machinery to perform the given work while correcting as necessary the movement of the working part based on the basic operation command and the automatic operation correcting command. Then, the estimation basic data including the work-state data of the working part, the operation-state data of the operating part, and the data of the reaction received by the operating part from the work-target object due to the work performed by the working part, is stored in the estimation-basic-data memory, and the operation correcting command adding the manual operation correcting command to the automatic operation correcting command is stored in the operation-correcting-command memory. Then, during the learning after that, the learning module performs the machine learning of the operation correcting command stored in the operation-correcting-command memory by using the estimation basic data stored in the estimation-basic-data memory. Therefore, the correcting manipulation by the highly-skilled operator can be learned to be outputted as the automatic operation correcting command. Then, during the later given work, when the learning module receives the input of the estimation basic data, it outputs the automatic operation correcting command. Then, the hydraulic drive system drives the operating part while reflecting the basic operation command and this automatic operation correcting command. Consequently, the work reflecting the automatic operation correcting command as a result of the learning of the correcting manipulation by the highly-skilled operator, is performed. Therefore, the construction machinery can be provided, which is capable of learning the work performed by the construction machinery while the operator corrects the basic operation of the working part caused by the basic-operation commanding module, and automatically performing the learned work. 
     According to this, the skill-inheriting construction machinery can be provided, which is capable of taking over the skill of the highly-skilled operator in the construction industry, and achieving the automation of the given work in the short period of time. 
     The manual operation correcting command may be an electrical command signal. The operating part may include a hydraulic actuator configured to drive the working part, and a control valve configured to hydraulically control operation of the hydraulic actuator according to the basic operation command, the automatic operation correcting command, and the manual operation correcting command. The control valve may be an electromagnetic valve. 
     According to this configuration, since the manual operation correcting command is the electrical command signal, the manual operation command can be directly converted into numerical data. Thus, the addition of the basic operation command, the automatic operation correcting command, and the manual operation correcting command, the addition of the automatic operation correcting command and the manual operation correcting command, and the storing of the operation correcting command in the memory, can easily be performed. Therefore, the configuration related to these processings can be simplified, compared to a case where the manual operation correcting command is a hydraulic signal and the control valve is a hydraulic control valve. 
     EFFECTS OF THE DISCLOSURE 
     The present disclosure can provide construction machinery, which is capable of learning a work performed by the construction machinery through a manipulation by a human, and automatically performing the learned work. 
     Moreover, the particular aspect of the present disclosure can provide skill-inheriting construction machinery, which is capable of taking over skill of a highly-skilled operator in the construction industry, and achieving automation of a given work in a short period of time. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a functional block diagram illustrating a concept of construction machinery with learning function according to one embodiment of the present disclosure. 
         FIG. 2  is a functional block diagram illustrating a configuration of a control system of the construction machinery with learning function according to Embodiment 1 of the present disclosure. 
         FIG. 3  is a side view illustrating a configuration of hardware of a hydraulic excavator with learning function, which is one example of the construction machinery with learning function. 
         FIG. 4  is a hydraulic circuit diagram illustrating a main hydraulic circuit in a hydraulic drive system of the hydraulic excavator with learning function illustrated in  FIG. 3 . 
         FIG. 5  is a hydraulic circuit diagram illustrating a hydraulic circuit of an operation system in the hydraulic drive system of the hydraulic excavator with learning function illustrated in  FIG. 3 . 
         FIG. 6  is a functional block diagram illustrating a configuration of a control system of the hydraulic excavator with learning function illustrated in  FIG. 3 . 
         FIG. 7  is a schematic view illustrating time-series data of each of an estimated operation command, command data for learning, estimation basic data, and estimation basic data for learning, in the hydraulic excavator with learning function illustrated in  FIG. 3 . 
         FIG. 8  is a functional block diagram illustrating a configuration of a learning module illustrated in  FIG. 6 . 
         FIG. 9  is a schematic view illustrating a situation of a constructing work performed by the hydraulic excavator with learning function illustrated in  FIG. 3 . 
         FIG. 10  is a side view illustrating a configuration of hardware of a hydraulic excavator with learning function, which is one example of construction machinery with learning function according to Embodiment 2 of the present disclosure. 
         FIG. 11  is a functional block diagram illustrating a configuration of a control system of the hydraulic excavator with learning function illustrated in  FIG. 10 . 
         FIG. 12  is a side view illustrating a configuration of hardware of a hydraulic excavator with learning function, which is one example of construction machinery with learning function according to Embodiment 3 of the present disclosure. 
         FIG. 13  is a functional block diagram illustrating a configuration of a control system of the hydraulic excavator with learning function illustrated in  FIG. 12 . 
         FIG. 14  is a functional block diagram illustrating a configuration of a control system of skill-inheriting construction machinery according to Embodiment 4 of the present disclosure. 
         FIG. 15  is a side view illustrating a configuration of hardware of a skill-inheriting hydraulic excavator, which is one example of the skill-inheriting construction machinery. 
         FIG. 16  is a hydraulic circuit diagram illustrating a main hydraulic circuit of a hydraulic drive system of the skill-inheriting hydraulic excavator illustrated in  FIG. 15 . 
         FIG. 17  is a hydraulic circuit diagram illustrating a hydraulic circuit of an operation system in the hydraulic drive system of the skill-inheriting hydraulic excavator illustrated in  FIG. 15 . 
         FIG. 18  is a functional block diagram illustrating a configuration of a control system of the skill-inheriting hydraulic excavator illustrated in  FIG. 15 . 
         FIG. 19  is a schematic view illustrating a cycle time of operation of the skill-inheriting hydraulic excavator illustrated in  FIG. 15 . 
         FIG. 20  is a schematic view illustrating time-series data of each of an operation correcting command, an operation correcting command for learning, estimation basic data, and estimation basic data for learning, in the skill-inheriting hydraulic excavator illustrated in  FIG. 15 . 
         FIG. 21  is a functional block diagram illustrating a configuration of a learning module illustrated in  FIG. 20 . 
         FIG. 22  is a schematic view illustrating a situation of a constructing work performed by the skill-inheriting hydraulic excavator illustrated in FIG.  15 . 
         FIGS. 23( a ) to 23( c )  are cross-sectional views schematically illustrating a process in which the digging work performed by a hydraulic excavator  20  is improved through a correcting manipulation to dig down corners. 
         FIGS. 24( a ) to 24( c )  are cross-sectional views schematically illustrating a process in which a digging work performed by the hydraulic excavator  20  is improved through a correcting manipulation according to a geological feature of a site planned to be dug. 
         FIG. 25  is a side view illustrating a configuration of hardware of a skill-inheriting hydraulic excavator, which is one example of skill-inheriting construction machinery according to Embodiment 5 of the present disclosure. 
         FIG. 26  is a functional block diagram illustrating a configuration of a control system of the skill-inheriting hydraulic excavator illustrated in  FIG. 25 . 
         FIG. 27  is a side view illustrating a configuration of hardware of a skill-inheriting hydraulic excavator, which is one example of skill-inheriting construction machinery according to Embodiment 6 of the present disclosure. 
         FIG. 28  is a functional block diagram illustrating a configuration of a control system of the skill-inheriting hydraulic excavator illustrated in  FIG. 27 . 
         FIG. 29  is a functional block diagram illustrating a configuration of an operation command generating module of a skill-inheriting hydraulic excavator, which is one example of skill-inheriting construction machinery according to Embodiment 7 of the present disclosure. 
     
    
    
     MODES FOR CARRYING OUT THE DISCLOSURE 
     Hereinafter, embodiments of the present disclosure are described with reference to the drawings. Note that the same reference characters are given to the same or corresponding elements throughout the drawings to omit redundant description. Moreover, the present disclosure is not limited to the following embodiments. 
     (Concept) 
       FIG. 1  is a functional block diagram illustrating a concept of construction machinery with learning function according to one embodiment of the present disclosure. 
     Referring to  FIG. 1 , construction machinery  1000  with learning function according to this embodiment of the present disclosure is provided with a working part  104 , an operating part  103 , a manipulating part  101 , a work-state detecting part  112 , an operation-state detecting part  113 , a reaction detecting part  114 , a learning data memory  115 , a learning module  118 , and a hydraulic drive system  105 . The operating part  103  moves the working part  104  to perform a work. The manipulating part  101  outputs a command  201  according to operation by an operator. The work-state detecting part  112  detects a state of the work performed the working part  104 , and outputs the detected work state as work-state data  212 . The operation-state detecting part  113  detects a state of operation of the operating part  103 , and outputs the detected operation state as operation-state data  213 . The reaction detecting part  114  detects a reaction received by the operating part  103  from a work-target object as a result of the work performed by the working part  104 , and outputs the detected reaction as reaction data  214 . The learning data memory  115  stores the command  201  in a time series as command data  211 , and also stores, in a time series, estimation basic data Pd including the work-state data  212 , the operation-state data  213 , and the reaction data  214 . The learning module  118  executes machine learning of the command data  211  stored in the learning data memory  115 , by using the estimation basic data Pd stored in the learning data memory  115 , and then, when the operating part  103  operates, the learning module  118  receives an input of the estimation basic data Pd and outputs an estimated command  1103  of the command  201 . The hydraulic drive system  105  drives the operating part  103  based on the command  201  or the estimated command  1103 , or based on the command  201  and the estimated command  1103 . 
     A reference character “ 102 ” indicates a body part of the construction machinery  1000  with learning function. The operating part  103  and the hydraulic drive system  105  are provided to the body part  102 . 
     A command data generating module  1101  generates the command data  211  based on the command  201 . The command data  211  is generated, for example, in a mode in which a command in a hydraulic pressure outputted from the manipulating part  101  is converted into the command data (Embodiments 1 to 3), and a mode in which the estimated command outputted from the learning module  118  is added to the command outputted from the manipulating part  101  (Embodiments 4 to 7). 
     The learning data memory  115  is provided with a command memory  1102  and an estimation-basic-data memory  117 . The command memory  1102  stores the command data  211  during the learning or the operation, and outputs it to the learning module  118  as command data  211 ′ for learning during the learning. The estimation-basic-data memory  117  stores the estimation basic data Pd ( 212  to  214 ) during the learning or the operation, and outputs it to the learning module  118  as estimation basic data Pd′ for learning during the learning. 
     Such construction machinery  1000  with learning function carries out a given work during the learning (in the last operation in Embodiments 4 to 7) while a highly-skilled operator manipulates the construction machinery. Then, the command data  211  corresponding to the command based on the manipulation, and the estimation basic data Pd including the work-state data  212  of the working part  104 , the operation-state data  213  of the operating part  103 , and the data  214  of the reaction received by the operating part  103  from the work-target object due to the work performed by the working part  104 , are stored in the learning data memory  115 . Then, the learning module  118  receives the input of the estimation basic data Pd′ stored in the learning data memory  115  so as to execute the machine learning of the learning data  211 ′ and Pd′ to output the estimated command  1103 . Therefore, the manipulation by the highly-skilled operator can be learned, and outputted as the estimated command  1103 . When the estimation basic data Pd is inputted into the learning module  118  after the machine learning is finished, the learning module  118  outputs the estimated command  1103 . Then, the hydraulic drive system  105  drives the operating part  103  based on the estimated command  1103 . As a result, the manipulation by the highly-skilled operator is learned, and the work is automatically performed based on the estimated command  1103  which is outputted from the learning module  118  as a result of the learning. 
     Accordingly, the construction machinery  1000  can be provided, which is capable of learning the work performed by the construction machinery through the manipulation by the human, and automatically performing the learned work. 
     Hereinafter, embodiments of the concept of the construction machinery  1000  with learning function are described in order. The following embodiments are roughly classified into Embodiments 1 to 3 relating to the construction machinery with learning function, and Embodiments 4 to 7 relating to skill-inheriting construction machinery capable of taking over skill of a highly-skilled operator in the construction industry, and achieving automation of a given work within a short period of time. 
     Embodiment 1 
     {Outline} 
     First, an outline of construction machinery with learning function according to Embodiment 1 is described. 
     [Configuration] 
       FIG. 2  is a functional block diagram illustrating a configuration of a control system of the construction machinery with learning function according to Embodiment 1 of the present disclosure. In  FIG. 2 , each arrow indicates a flow of a command, a motive power, information, or data. Each solid-line arrow indicates a flow of a command or data during the automatic control, and each broken line indicates a flow of a command or data during the learning. Note that, as described later, when the estimation basic data in the past is used for the learning, the data flow is as indicated by the broken lines even during the automatic control. This similarly applies to  FIGS. 6, 10, and 12 . 
     Referring to  FIG. 2 , construction machinery  100  with learning function is provided with the manipulating part  101 , the body part  102 , the operating part  103 , the hydraulic drive system  105 , an operation command detecting part  110 , the work-state detecting part  112 , the operation-state detecting part  113 , the reaction detecting part  114 , the learning data memory  115 , the learning module  118 , and a manipulating-part drive part  119 . Moreover, the construction machinery  100  with learning function has an overall-controlling module and an operation-mode-switching manipulation part (neither of them is illustrated), and according to operation to the operation-mode-switching manipulation part by the operator, the overall-controlling module switches an operation mode of the construction machinery  100  with learning function between a learning mode and an automatic control mode. Below, a time during which the construction machinery  100  with learning function is operated in the learning mode is referred to as “during the learning,” and a time during which the construction machinery  100  with learning function is operated in the automatic control mode is referred to as “during the automatic control.” 
     In Embodiment 1, the manipulating part  101  outputs the operation command  201  as a command corresponding to the operation by the operator. The learning data memory  115  stores the operation command in a time series as the command data  211 , and also stores, in a time series, the estimation basic data Pd including the work-state data  212 , the operation-state data  213 , and the reaction data  214 . The learning module  118  executes, during the learning, the machine learning of the command data  211 ′ stored in the learning data memory  115 , by using the estimation basic data Pd′ stored in the learning data memory  115 , and during the automatic control after the machine learning, the learning module  118  receives the input of the estimation basic data Pd, and outputs an estimated operation command Pf which is the estimated command  1103 . The hydraulic drive system  105  drives the operating part  103  based on the operation command  201  or the estimated operation command Pf. Note that the operation command  201  is converted into the command data  211  by the operation command detecting part  110  as the command data generating module  1101 . 
     Below, the configuration of the construction machinery  100  with learning function is described in detail. 
     The manipulating part  101  outputs the operation command  201  according to the operation by the operator. 
     The body part  102  is coupled to the operating part  103 . 
     The operating part  103  has the working part  104  which performs the work, and moves the working part  104  to perform the work. Here, the phrase “move the working part  104 ” means “cause the working part  104  to operate and move.” 
     The “construction machinery” may be any work machine, as long as it can perform a construction work by the operating part moving the working part according to the manipulation by the operator. The “construction machinery” may be, for example, a hydraulic excavator, a bulldozer, a tractor excavator, a wheeled loader, a trencher, an excavator, a crane, a lift vehicle, etc. 
     The hydraulic drive system  105  is provided over the body part  102  and the operating part  103 . The hydraulic drive system  105  outputs a drive force  202  based on the operation command  201  outputted from the manipulating part  101  or the estimated operation command Pf outputted from the learning module  118 , thus driving the operating part  103 . 
     The operation command detecting part  110  detects the operation command  201  outputted from the manipulating part  101 , and outputs it as the command data  211 . In detail, when the operation command  201  is a command in a hydraulic pressure (pilot pressure command), it is converted into the command data  211  which is electrical data, to be outputted. Therefore, the operation command detecting part  110  is not an essential element. When the operation command  201  is an electrical command, the operation command detecting part  110  may be omitted, and the operation command  201  may be directly inputted into a command data memory  116  (described later) and the learning module  118 . In a hydraulic excavator  10  with learning function (described later), the operation command  201  which is an acceleration command (electrical command) outputted from an acceleration device  50 , is directly inputted into the command data memory  116  and the learning module  118 . 
     The work-state detecting part  112  detects a state of the work performed by the working part  104 , and outputs the detected work state as the work-state data  212 . 
     The operation-state detecting part  113  detects a state of the operation of the operating part  103 , and outputs the detected operation state as the operation-state data  213 . 
     The estimation basic data includes the work-state data  212 , the operation-state data  213 , and the reaction data  214 . For convenience, the reference character “Pd” is given to the estimation basic data used during the learning, and the reference character “Pd” is given to the estimation basic data used during the automatic control. Moreover, the estimation basic data includes the command data  211 . 
     The reaction detecting part  114  detects the reaction received by the operating part  103  or the body part  102  from the work-target object due to the work performed by the working part  104 , and outputs the detected reaction as the reaction data  214 . 
     The learning data memory  115  is provided with the command data memory  116  and the estimation-basic-data memory  117 . 
     The command data memory  116  stores the command data  211  in a time series as the command data memory  1102 . The estimation-basic-data memory  117  stores, in a time series, the estimation basic data Pd including the command data  211 , the work-state data  212 , the operation-state data  213 , and the reaction data  214 . 
     The learning module  118  is a learning model which performs the machine learning. The learning model is, for example, a neural network model, a regression model, a tree model, a Bayesian model, a time-series model, a cluster model, an ensemble learning model, etc. In this embodiment, the learning model is the neural network model. The mode of the learning may be supervised or unsupervised learning. Moreover, it may be deep learning. 
     For example, in case of the supervised learning, the learning module  118  reads, during the learning, the command data stored in the command data memory  116  as command data Pf for learning, and also reads the estimation basic data stored in the estimation-basic-data memory  117  as the estimation basic data Pd′ for learning. Then, learning data is created using the command data Pf for learning as teacher data, and the estimation basic data Pd′ for learning as input data. Then, the estimation basic data Pd′, which is the input data, is inputted into the machine learning model (e.g., the neural network), a difference between an output and the teacher data Pf is evaluated, and the evaluation is fed back to the machine learning model. Accordingly, the machine learning model can execute the machine learning of the learning data. After the machine learning is finished, the learning module  118  outputs the output of the machine learning model to outside as the estimated operation command Pf. In detail, during the automatic control, when the machine-learning model of the learning module  118  receives the input of the estimation basic data Pd, it outputs the estimated operation command Pf. 
     The manipulating-part drive part  119  operates, during the automatic control, the manipulating part  101  based on the estimated operation command Pf outputted from the learning module  118 . Therefore, the operation command  201  is outputted from the manipulating part  101 , and the hydraulic drive system  105  drives the operating part  103  based on the operation command  201 . That is, the manipulating-part drive part  119  and the manipulating part  101  function as an estimated-operation-command converting part  120  which converts the estimated operation command Pf into the operation command  201 . The manipulating-part drive part  119  is comprised of, for example, a motor, a manipulator (robot), etc. 
     Therefore, the manipulating-part drive part  119  is not an essential element. For example, instead of the manipulating-part drive part  119 , an estimated-operation-command converting part which converts the estimated operation command Pf, which is an electrical command signal, into a command in the hydraulic pressure (pilot pressure command) may be provided, and during the automatic control, the output from the estimated-operation-command converting part may be inputted into the hydraulic drive system  105 , instead of the operation command from the manipulating part  101 . The estimated-operation-command converting part may be comprised of a pilot valve which is an electromagnetic valve (electromagnetic proportional valve). Note that, when the construction machinery  100  with learning function outputs from the manipulating part  101  the operation command  201  which is the electrical command, the estimated-operation-command converting part  120  is unnecessary, and the estimated operation command Pf outputted from the learning module  118  is directly inputted into the hydraulic drive system  105 . 
     In this manner, the hydraulic drive system  105  drives the operating part  103  based on the estimated operation command Pf converted into the operation command by the manipulating-part drive part  119  and the manipulating part  101 . 
     [Operation] 
     During the learning, the highly-skilled operator manipulates the construction machinery  100  with learning function to perform a given work. Then, the command data  211  corresponding to the operation command  201  according to the manipulation, and the estimation basic data are stored in the learning data memory  115 . The estimation basic data includes the work-state data  212  indicating the state of the work performed by the working part  104 , the operation-state data  213  indicating the state of the operation of the operating part, and the reaction data  214  indicating the reaction received by the operating part  103  or the body part  102  from the work-target object due to the work performed by the working part  104 . The given work is, for example, a comparatively simple routine work. The routine work may be, for example, a digging work, a ground leveling work, and a rolling compaction work. 
     Then, during the learning, the learning module  118  reads the command data stored in the command data memory  116  as the command data Pf for learning, and reads the estimation basic data stored in the estimation-basic-data memory  117  as the estimation basic data Pd′ for learning. Then, the learning data is created using the command data Pf for learning as the teacher data, and the estimation basic data Pd′ for learning as the input data, and the learning module  118  executes the machine learning of the learning data. After the machine learning is finished, when the learning module  118  receives the input of the estimation basic data Pd during the automatic control, it outputs the estimated operation command Pf. Accordingly, the hydraulic drive system  105  drives the operating part  103  based on the estimated operation command Pf converted into the operation command  201  by the manipulating-part drive part  119  and the manipulating part  101 . As a result, the manipulation by the highly-skilled operator is learned, and the work is automatically performed based on the estimated operation command Pf which is outputted from the learning module  118  as a result of the learning. 
     As described above, according to Embodiment 1, the hydraulic drive system  105  drives the operating part  103  based on the estimated operation command Pf which is the estimated command of the operation command  201 . Therefore, the work is automatically performed based on the result of the learning of the manipulation by the highly-skilled operator. 
     {Concrete Configuration} 
     Next, a concrete configuration of the construction machinery  100  with learning function is described, taking the hydraulic excavator  10  as one example of the construction machinery. 
     [Configuration of Hardware] 
     &lt;Entire Configuration&gt; 
     First, the entire configuration of the hydraulic excavator  10  with learning function is described. 
       FIG. 3  is a side view illustrating a configuration of hardware of the hydraulic excavator  10  with learning function, which is one example of the construction machinery with learning function. 
     The hydraulic excavator  10  with learning function (hereinafter, may simply be referred to as a “hydraulic excavator”) is provided with the body part  102 . The body part  102  is provided with a traveling body (carrier)  19 . The traveling body  19  is comprised of, for example, a vehicle traveling device provided with a continuous track (caterpillar). 
     A swiveling body  15  is provided on the body part  102  so as to be swivable about a vertical first rotary axis A 1 . The swiveling body  15  is provided with a driver&#39;s seat (not illustrated), and the manipulating part  101  is provided to the driver&#39;s seat (see  FIG. 6 ). Note that, although not illustrated in the manipulating part  101  of  FIG. 6 , a travel-manipulating device which operates the traveling body  19  is provided to the driver&#39;s seat. The swiveling body  15  is further provided with a swiveling motor  14  which causes the swiveling body  15  to swivel. The swiveling motor  14  is comprised of a hydraulic motor. The swiveling body  15  is also provided with an engine  26  (see  FIG. 6 ) for traveling. The engine  26  drives a pump part  107  (see  FIG. 6 ) of a hydraulic drive system  1  during the work. 
     A base-end part of a boom  16  is coupled to the swiveling body  15  so as to be pivotable about a horizontal second rotary axis A 2 . A tip-end part and a base-end part of a boom cylinder  11  are rotatably coupled to the base-end part of the boom  16  and the swiveling body  15 , respectively, and the boom  16  swings centering on the second rotary axis A 2  according to the extension and contraction of the boom cylinder  11 . 
     A base-end part of an arm  17  is coupled to a tip-end part of the boom  16  so as to be pivotable about a horizontal third rotary axis A 3 . A tip-end part and a base-end part of an arm cylinder  12  are rotatably coupled to the base-end part of the arm  17  and the tip-end part of the boom  16 , respectively, and the arm  17  swings centering on the third rotary axis A 3  according to the extension and contraction of the arm cylinder  12 . 
     A base-end part of a bucket  18  is coupled to a tip-end part of the arm  17  so as to be pivotable about a horizontal fourth rotary axis A 4 . A tip-end part and a base-end part of a bucket cylinder  13  are rotatably coupled to the base-end part of the bucket  18  and the tip-end part of the arm  17 , respectively, and the bucket  18  pivots centering on the fourth rotary axis A 4  according to the extension and contraction of the bucket cylinder  13 . The bucket  18  is one example of an attachment, and other attachments may be used. 
     The boom  16 , the arm  17 , and the bucket  18  constitute a front-work device. Moreover, the bucket  18  constitutes the working part  104 , and the swiveling body  15  and the front-work device (the boom  16 , the arm  17 , and the bucket  18 ) constitute the operating part  103 . 
     In addition to the above structures, the hydraulic excavator  10  is provided with a left-and-right pair of hydraulic traveling motors (not illustrated). 
     The operator carries out a desired work by operating the manipulating part  101  (including the travel-manipulating device (not illustrated)) to position the hydraulic excavator at a desired location, to swivel the swiveling body  15 , to change postures of the booms  16  and  17 , and to rotary-drive the bucket  18 . 
     The hydraulic excavator  10  with learning function is further provided with a first imaging device  311 . The first imaging device  311  images the state of the work performed by the bucket  18 . The image captured by the first imaging device  311  is applied with image processing by an image processing module  312  (see  FIG. 6 ) described later, so that data indicative of the work state can be obtained, and is outputted from the image processing module  312  as the work-state data  212 . An optical axis  321  of the first imaging device  311  is oriented toward the work-target object. 
     The first imaging device  311  is comprised of, for example, a three-dimensional (3D) camera, a camera with a depth sensor, etc. The first imaging device  311  is, for example, fixed via a suitable support member to the body part  102 , fixed via a suitable support member to a fixed object (e.g., the ground) separated from the vehicle of the hydraulic excavator  10 , or mounted on a drone. 
     &lt;Hydraulic Drive System  1 &gt; 
     Next, the hydraulic drive system  1  which causes the hydraulic excavator  10  to operate, is described. 
       FIG. 4  is a hydraulic circuit diagram illustrating a main hydraulic circuit of the hydraulic drive system  1  of the hydraulic excavator  10  with learning function.  FIG. 5  is a hydraulic circuit diagram illustrating a hydraulic circuit of an operation system (operating-system hydraulic circuit) in the hydraulic drive system of the hydraulic excavator  10  with learning function. The main hydraulic circuit and the operating-system hydraulic circuit are provided to the swiveling body  15 . 
     As described above, the hydraulic drive system  1  includes the boom cylinder  11 , the arm cylinder  12 , and the bucket cylinder  13  as hydraulic actuators, and also includes the swiveling motor  14  and the left-and-right pair of hydraulic traveling motors (not illustrated). 
     Referring to  FIG. 4 , the hydraulic drive system  1  includes a first main pump  21  and a second main pump  23 , which supply hydraulic oil to the actuators described above. Note that, in  FIG. 4 , illustration of the actuators other than the swiveling motor  14  is omitted for simplification of the drawing. 
     The first main pump  21  and the second main pump  23  are driven by the engine  26 . The engine  26  also drives a sub pump  25 . The first main pump  21 , the second main pump  23 , and the sub pump  25  constitute the pump part  107  (see  FIG. 6 ). An output of the engine  26  is adjusted by the acceleration device  50  (see  FIG. 6 ). The acceleration device  50  is provided with, for example, an accelerator pedal, and outputs to an engine controlling device (not illustrated) an acceleration command which is an electrical command corresponding to an amount of depression of the accelerator pedal. The engine controlling device controls the output (speed) of the engine  26  based on the acceleration command. 
     The first main pump  21  and the second main pump  23  are, for example, variable displacement pumps which discharge hydraulic oil in an amount corresponding to a tilt angle. Here, the first main pump  21  and the second main pump  23  are swash plate pumps which define their tilt angles by angles of swash plates. However, the first main pump  21  and the second main pump  23  may be bent axis pumps which define their tilt angles by angles each formed between a drive shaft and a cylinder block. 
     An amount of discharge Q 1  of the first main pump  21 , and an amount of discharge Q 2  of the second main pump  23  are controlled in an electric positive control method. In detail, the tilt angle of the first main pump  21  is adjusted by a first flow-amount adjusting device  22 , and the tilt angle of the second main pump  23  is adjusted by a second flow-amount adjusting device  24 . The sub pump  25  is connected to the first flow-amount adjusting device  22  and the second flow-amount adjusting device  24  via a sub bleed line  37 . The sub pump  25  functions as a drive source of the first flow-amount adjusting device  22  and the second flow-amount adjusting device  24 . Details of the first flow-amount adjusting device  22  and the second flow-amount adjusting device  24  will be described later. 
     A first center bleed line  31  is extended from the first main pump  21  to a tank. A plurality of control valves including a first arm control valve  41  and a swivel control valve  43  (only the first arm control valve  41  and the swivel control valve  43  are illustrated) are provided on the first center bleed line  31 . The control valves are connected to the first main pump  21  through pump lines  32 , respectively. That is, the control valves on the first center bleed line  31  are parallelly connected to the first main pump  21 . Moreover, the control valves are connected to the tank through tank lines  33 , respectively. 
     Similarly, a second center bleed line  34  is extended from the second main pump  23  to a tank. A plurality of control valves including a second arm control valve  42  and a bucket control valve  44  (only the second arm control valve  42  and the bucket control valve  44  are illustrated) are provided on the second center bleed line  34 . The control valves are connected to the second main pump  23  through pump lines  35 , respectively. That is, the control valves on the second center bleed line  34  are parallelly connected to the second main pump  23 . Moreover, the control valves are connected to the tank through tank lines  36 , respectively. 
     The first arm control valve  41  controls, together with the second arm control valve  42 , supply and discharge of hydraulic oil to the arm cylinder  12 . That is, the hydraulic oil is supplied to the arm cylinder  12  from the first main pump  21  through the first arm control valve  41 , as well as from the second main pump  23  through the second arm control valve  42 . The first arm control valve  41  and the second arm control valve  42  constitute an arm control valve  40  (see  FIG. 5 ). 
     The swivel control valve  43  controls supply and discharge of hydraulic oil to the swiveling motor  14 . That is, the hydraulic oil is supplied to the swiveling motor  14  from the first main pump  21  through the swivel control valve  43 . In detail, the swiveling motor  14  is connected to the swivel control valve  43  through a pair of supply-and-discharge lines  61  and  62 . Bypass lines  63  are branched from the supply-and-discharge lines  61  and  62 , respectively, and are connected to a tank. Each bypass line  63  is provided with a relief valve  64 . Moreover, the supply-and-discharge lines  61  and  62  are connected to the tank through a pair of makeup lines  65 , respectively. Each makeup line  65  is provided with a check valve  66  which allows the flow from the tank to the supply-and-discharge line ( 61  or  62 ), but prohibits a backflow. 
     The bucket control valve  44  controls supply and discharge of hydraulic oil to the bucket cylinder  13 . That is, the hydraulic oil is supplied to the bucket cylinder  13  from the second main pump  23  through the bucket control valve  44 . 
     Although not illustrated in  FIG. 4 , the control valves on the second center bleed line  34  include a first boom control valve  45  (see  FIG. 5 ), and the control valves on the first center bleed line  31  include a second boom control valve  46  (see  FIG. 5 ). The second boom control valve  46  is a valve dedicated for operation of lifting the boom. That is, during the lifting of the boom, hydraulic oil is supplied to the boom cylinder  11  through the first boom control valve  45  and the second boom control valve, and during lowering of the boom, hydraulic oil is supplied through only the first boom control valve  45 . 
     As illustrated in  FIG. 5 , a boom control valve  47  (the first boom control valve  45  and the second boom control valve) is operated by a boom manipulation device  71 . The arm control valve  40  (the first arm control valve  41  and the second arm control valve  42 ) is operated by an arm manipulation device  51 . The swivel control valve  43  is operated by a swivel manipulation device  54 . The bucket control valve  44  is operated by a bucket manipulation device  57 . Each of the boom manipulation device  71 , the arm manipulation device  51 , the swivel manipulation device  54 , and the bucket manipulation device  57  includes a manipulation lever, and a manipulation signal (command) corresponding to a tilt angle of the manipulation lever is outputted. 
     In this embodiment, each of the boom manipulation device  71 , the arm manipulation device  51 , the swivel manipulation device  54 , and the bucket manipulation device  57 , is comprised of a pilot valve which outputs a pilot pressure command corresponding to the tilt angle of the manipulation lever. Therefore, the arm manipulation device  51  is connected to a pair of pilot ports of the first arm control valve  41  through a pair of pilot lines  52  and  53 , the swivel manipulation device  54  is connected to a pair of pilot ports of the swivel control valve  43  through a pair of pilot lines  55  and  56 , the bucket manipulation device  57  is connected to a pair of pilot ports of the bucket control valve  44  through a pair of pilot lines  58  and  59 , and the boom manipulation device  71  is connected to a pair of pilot ports of the first boom control valve  45  through a pair of pilot lines  72  and  73 . Moreover, a pair of pilot ports of the second arm control valve  42  are connected to the pilot lines  52  and  53  through a pair of pilot lines  52   a  and  53   a , respectively. Moreover, in the second boom control valve  46 , only a pilot port for lifting the boom is connected to the pilot line  73  through a pilot line  73   a , and the other pilot port is not connected to the pilot line  72 . Therefore, the second boom control valve  46  does not operate when the boom manipulation device  71  is operated to lower the boom. 
     Note that each manipulation device may be comprised of an electrical joystick which outputs an electrical signal (command) corresponding to a tilt angle of a manipulation lever, and a pair of electromagnetic proportional valves may be provided to the pilot ports of corresponding control valve. 
     The pilot lines  52 ,  53 ,  55 ,  56 ,  59 ,  58 ,  72 , and  73  are provided with pressure sensors  81  to  86 ,  91 , and  92  which detect pressures of the pilot pressure commands, respectively. Note that the pressure sensors  81  and  82  which detect the pressures of the pilot pressure command outputted from the arm manipulation device  51  may be provided to the pilot lines  52   a  and  53   a , respectively. The pressure sensors  81  to  86 ,  91 , and  92  constitute the operation command detecting part  110  (see  FIG. 6 ). 
     The first flow-amount adjusting device  22  and the second flow-amount adjusting device  24  described above are electrically controlled by a flow-amount controlling device  8 . For example, the flow-amount controlling device  8  has a memory (e.g., a ROM and a RAM) and a CPU, and a program stored in the ROM is executed by the CPU. The flow-amount controlling device  8  controls the first flow-amount adjusting device  22  and the second flow-amount adjusting device  24  so that the tilt angles of the first main pump  21  and/or the second main pump  23  increase as the pilot pressure commands (manipulation signals) detected by the pressure sensors  81  to  86 ,  91 , and  92  increase. For example, when the swiveling manipulation alone is performed, the flow-amount controlling device  8  controls the first flow-amount adjusting device  22  so that the tilt angle of the first main pump  21  increases as the pilot pressure command outputted from the swivel manipulation device  54  increases. 
     [Configuration of Control System] 
     Next, a configuration of a control system of the hydraulic excavator  10  with learning function is described. 
     &lt;Entire Configuration&gt; 
       FIG. 6  is a functional block diagram illustrating the configuration of the control system of the hydraulic excavator  10  with learning function.  FIG. 6  illustrates the control system of the hydraulic excavator  10  related to the learning function of this embodiment. Therefore, a control system of the traveling body  19  unrelated to the learning function of this embodiment is omitted. Moreover, the hydraulic excavator  10  with learning function has the overall-controlling module and the operation-mode-switching manipulation part (neither of them is illustrated). The overall-controlling module switches the operation mode of the hydraulic excavator  10  with learning function between the learning mode and the automatic control mode, according to the operation of the operator to the operation-mode-switching manipulation part. 
     Referring to  FIG. 6 , the acceleration device  50 , the arm manipulation device  51 , the swivel manipulation device  54 , the bucket manipulation device  57 , and the boom manipulation device  71  constitute the manipulating part  101 . The manipulating part  101  is provided to the driver&#39;s seat of the swiveling body  15 . 
     When the operator depresses the accelerator pedal of the acceleration device  50 , the acceleration device  50  outputs the operation command  201  which is the acceleration command corresponding to the depressed amount of the accelerator pedal. Then, the engine  26  drives the pump part  107  by the output corresponding to the operation command  201 . Then, the pump part  107  discharges hydraulic oil to a hydraulic circuit  106  in an amount corresponding to the output of the pump part  107 . 
     When the operator operates the manipulation lever of the swivel manipulation device  54 , the swivel manipulation device  54  outputs the operation command  201  which is the pilot pressure command (swiveling command) corresponding to the tilt angle of the manipulation lever. Then, the swivel control valve  43  supplies and discharges hydraulic oil to/from the swiveling motor  14  based on the operation command  201 . Then, the swiveling motor  14  causes the swiveling body  1  to swivel  5  according to the supply and discharge of the hydraulic oil. 
     When the operator operates the manipulation lever of the boom manipulation device  71 , the boom manipulation device  71  outputs the operation command  201  which is the pilot pressure command (boom operation command) corresponding to the tilt angle of the manipulation lever. Then, the boom control valve  47  supplies and discharges hydraulic oil to/from the boom cylinder  11  based on the operation command  201 . Then, the boom cylinder  11  lifts and lowers the boom  16 , and thus, lifts and lowers the boom  16  and the arm  17  according to the supply and discharge of the hydraulic oil. 
     When the operator operates the manipulation lever of the arm manipulation device  51 , the arm manipulation device  51  outputs the operation command  201  which is the pilot pressure command (arm operation command) corresponding to the tilt angle of the manipulation lever. Then, the arm control valve  44  supplies and discharges hydraulic oil to/from the arm cylinder  12  based on the operation command  201 . Then, the arm cylinder  12  swings the arm  17  according to the supply and discharge of the hydraulic oil. 
     When the operator operates the manipulation lever of the bucket manipulation device  57 , the bucket manipulation device  57  outputs the operation command  201  which is the pilot pressure command (bucket operation command) corresponding to the tilt angle of the manipulation lever. Then, the bucket control valve  44  supplies and discharges hydraulic oil to/from the bucket  18  based on the operation command  201 . Then, the bucket cylinder  13  pivots the bucket  18  according to the supply and discharge of the hydraulic oil. 
     According to these manipulations, the work can be performed as intended by the operator. 
     Meanwhile, the pressure sensors  81  to  86 ,  91 , and  92  detect the operation commands  201  which are pilot pressure commands outputted from the arm manipulation device  51 , the swivel manipulation device  54 , the bucket manipulation device  57 , and the boom manipulation device  71 , respectively, and output them as the command data  211 . The pressure sensors  81  to  86 ,  91 , and  92  constitute the operation command detecting part  110 . 
     The command data memory  116  of the learning data memory  115  stores the respective command data  211  in a time series. Moreover, the command data memory  116  stores, as the command data  211 , the operation command  201  which is the acceleration command outputted from the acceleration device  50 , in a time series. The command data  211  includes the acceleration command, the arm operation command, the swiveling command, the bucket operation command, and the boom operation command. 
     As described above, the first imaging device  311  images the state of the work performed by the bucket  18 . Then, the image processing module  312  applies the image processing to the image captured by the first imaging device  311  so as to generate the data indicative of the state of the given work, and output it as the work-state data  212 . Therefore, the first imaging device  311  and the image processing module  312  constitute the work-state detecting part  112 . 
     The hydraulic excavator  10  is provided with a microphone  313 . The microphone  313  is installed, for example, near the engine  26  provided to the swiveling body  15 , and receives operation noise of the engine  26  and converts it to operation noise data so as to output it as the operation-state data  213 . The operation noise of the engine  26  increases as the output of the engine  26  increases, while it decreases as the output of the engine  26  decreases. Therefore, the operation noise data shows a driving-source state indicative of a state of a driving source of the pump part  107 , and thus, it is the operation-state data indicative of the operation state of the hydraulic excavator  10 . Accordingly, the microphone  313  constitutes a driving-source state detecting part, and thus, the operation-state detecting part  113 . 
     The swiveling body  15  or the body part  102  of the hydraulic excavator  10  is provided with a gyroscope  314 . The gyroscope  314  detects an inclination and vibration (including an excitation force, acceleration, and angular acceleration) of the swiveling body  15  or the body part  102 , converts them to inclination and vibration data, and outputs the inclination and vibration data as the reaction data  214 . For example, when the boom sticks the bucket  18  into the ground, the swiveling body  15  and the body part  102  incline due to a reaction force from the ground, and the swiveling body  15  and the body part  102  vibrate by receiving the excitation force through the boom (the operating part  103 ). Therefore, the inclination and vibration data outputted from the gyroscope  314  indicates the reaction received by the operating part  103  and the body part  102 . Accordingly, the gyroscope  314  constitutes the reaction detecting part  114 . 
     The estimation-basic-data memory  117  of the learning data memory  115  stores, as the estimation basic data, each of the work-state data  212  outputted from the image processing module  312 , the operation-state data  213  outputted from the microphone  313 , the reaction data  214  outputted from the gyroscope  314 , the command data  211  outputted from the operation command detecting part  110 , and the command data  211  which is the operation command outputted from the acceleration device  50 , in a time series. 
     As described above, the learning module  118  performs the machine learning of the learning data during the learning, and after the machine learning, when the learning module  118  receives the input of the estimation basic data Pd, it outputs the estimated operation command Pf. 
     The learning data memory  115 , the learning module  118 , the image processing module  312 , and the overall-controlling module (not illustrated) are, for example, comprised of an arithmetic unit including a processor and a memory. The learning module  118 , the overall-controlling module, and the image processing module  312  are functional blocks implemented by the processor executing a given program stored in the memory of the arithmetic unit. The learning data memory  115  is comprised of this memory. The arithmetic unit is comprised of, for example, a microcontroller, an MPU, an FPGA (Field Programmable Gate Array), a PLC (Programmable Logic Controller), etc. The learning data memory  115 , the learning module  118 , the image processing module  312 , and the overall-controlling module (not illustrated) may be comprised of a sole arithmetic unit which executes a centralized control, or a plurality of arithmetic units which execute a distributed control. The sole arithmetic unit or the plurality of arithmetic units may be provided to the swiveling body  15  of the hydraulic excavator  10 , for example. 
     Here, the manipulating-part drive part  119  is comprised of first to fifth servomotors M 1  to M 5 . The first to fifth servomotors M 1  to M 5  rotate the manipulation levers of the boom manipulation device  71 , the bucket manipulation device  57 , the swivel manipulation device  54 , and the arm manipulation device  51 , and the accelerator pedal of the acceleration device  50 , respectively, based on the estimated operation command Pf outputted from the learning module  118 . Therefore, during the automatic control, the boom control valve  47 , the bucket control valve  44 , the swivel control valve  43 , and the arm control valve  40  of the hydraulic drive system  1  drive the boom  16 , the bucket  18 , the swiveling body  15 , and the arm  17  of the operating part  103 , respectively, based on the estimated operation command Pf, and the engine  26  drives the pump part  107  of the hydraulic drive system  1  based on the estimated operation command Pf. 
     Next, the configuration of the learning module  118  is described in detail.  FIG. 7  is a schematic view illustrating time-series data of each of the estimated operation command Pf, the command data Pf for learning, the estimation basic data Pd, and the estimation basic data Pd′ for learning, in the hydraulic excavator  10  with learning function.  FIG. 8  is a functional block diagram illustrating the configuration of the learning module  118 . 
     &lt;Temporal Relation Between Time-Series Data&gt; 
     First, a temporal relation between the time-series data is described. 
     Referring to  FIG. 7 , the hydraulic excavator  10  carries out a given work by the automatic control. In order to cause the hydraulic excavator  10  to learn the given work, the highly-skilled operator causes the hydraulic excavator  10  to perform the given work as operation used for learning (operation for learning). A time point t 0 ′ indicates a start time point of the operation for learning. This operation for learning ends at a time point tu′. In this operation for learning, each of the command data  211  and the estimation basic data  212  to  214  is acquired at a given sampling interval, and they are stored in a time series in the command data memory  116  and the estimation-basic-data memory  117 , respectively. 
     When the operation for learning is finished, the command data and the estimation basic data are read as the command data Pf for learning and the estimation basic data Pd′ for learning, from the command data memory  116  and the estimation-basic-data memory  117 , respectively. The command data Pf for learning is time-series data Pf 0 ′, Pf 1 ′, Pf 3 ′ . . . Pfu′ (hereinafter, abbreviated as Pf 0 ′ to Pfu′). Below, a suffix number of the time-series data represents an order of sampling time point (intermittent time point). This means that the time-series data with the same suffix number are acquired at the same sampling time point. Similarly, the estimation basic data Pd′ for learning is time-series data Pd 0 ′ to Pdu′. 
     The machine learning is performed by using the command data Pf for learning and the estimation basic data Pd′ for learning. When the machine learning is finished, the hydraulic excavator  10  is automatically controlled to perform the given work. The current time point t 0  indicates a start time of the given work performed by the automatic control. During the automatic control, each of the command data  211  and the estimation basic data  212  to  214  is acquired at a given sampling interval, and sequentially inputted into the learning module  118  as the estimation basic data Pd. The estimation basic data Pd is time-series data Pd 0  to Pdu. 
     The learning module  118  outputs the estimated operation command Pf according to the estimation basic data Pd. The estimated operation command Pf is time-series data Pf 0  to Pfu. 
     The hydraulic excavator  10  operates based on the estimated operation command Pf, and thus, the hydraulic excavator  10  is automatically controlled. 
     &lt;Configuration of Learning Module  118 &gt; 
     Referring to  FIG. 8 , the learning module  118  is provided with, for example, a neural network  400 , a learning data and teacher data generating module  401 , a data inputting module  402 , and a learning evaluation module  403 . 
     The neural network  400  has an input layer, an intermediate layer, and an output layer. The number of neurons in each layer is set suitably. A well-known learning method may be applied to the learning by the neural network  400 . Therefore, it is briefly described here. Here, the neural network  400  is, for example, a recurrent neural network. The mode of learning is, for example, supervised learning. 
     The learning data and teacher data generating module  401  generates time-series data pn 1  to pnu of teacher data pn, based on the time-series data Pf 0 ′ to Pfu′ of the command data Pf for learning. Moreover, the time-series data Pd 0 ′ to Pdu−1′ of the learning data is generated based on the time-series data Pd 0 ′ to Pdu′ of the estimation basic data Pd′ for learning. 
     The data inputting module  402  sequentially inputs the time-series data Pd 0 ′ to Pdu−1′ of the learning data to each neuron of the input layer. Here, when the data inputting module  402  inputs time-series data Pdi of the learning data at a certain sampling time point ti, the neural network  400  calculates an estimated operation command Pni+1 at the next sampling time point ti+1 by a forward calculation. Then, the learning evaluation module  403  retrieves, among the time-series data pn 1  to pnu of the teacher data pn, time-series data pni+1 at the next sampling time point ti+1, and, for example, calculates a sum of squared errors e 2  of the acceleration command, the arm operation command, the swiveling command, the bucket operation command, and the boom operation command, between the estimated operation command Pni+1 and the time-series data pni+1 of the teacher data pn. Next, the learning evaluation module  403  updates weights of the neural network  400  by a backward calculation. The data inputting module  402  and the learning evaluation module  403  execute this processing for all of the time-series data Pd 0 ′ to Pdu−1′ of the learning data, and finish the learning when, for example, the sum of squared errors e 2  becomes below a given threshold in the processing for all of the time-series data Pd 0 ′ to Pdu−1′ of the learning data. 
     After the learning is finished, during the automatic control, the data inputting module  402  inputs, for example, the estimation basic data Pd 0  at the current sampling time point t 0 . Then, the neural network  400  outputs the estimated operation command Pn 1  at the next sampling time point t 1  as the estimated operation command Pf 1 . Note that a suitable initial operation command is outputted as the estimated operation command Pf 0 . 
     Therefore, the hydraulic excavator  10  is automatically controlled on the basis of the estimated operation command Pf based on the learning result of the neural network  400  (the learning module  118 ). 
     Note that, during the learning, when the data inputting module  402  inputs the time-series data Pdi of the learning data at each sampling time point ti, it may be time-series data Pdi−1 to Pdi−n (“n” is a given positive number) prior to the sampling time point ti. In this case, during the automatic control, the data inputting module  402  is required to similarly input, at each sampling time point tj, past estimation basic data Pdj−1 to Pdj−n, together with the current estimation basic data Pdj. Since the estimation basic data Pd 0  to Pdj−1 is stored in a time series in the estimation-basic-data memory  117  at each sampling time point tj, during the automatic control, the data inputting module  402  reads it from the estimation-basic-data memory  117 , and generates the past estimation basic data Pdj−1 to Pdj−n. Accordingly, the learning efficiency of the neural network  400  improves. This is because, when the operator estimates the motion of the bucket  18 , he/she estimates the next motion of the bucket  18  considering not only the work state, the operation state of the hydraulic excavator  10 , and the reaction from the work-target object at the present moment, but also the series of work states, operation states of the hydraulic excavator  10 , and reactions from the work-target object in the past, and thus, the motion of the bucket  18  is estimated accurately. 
     Note that information on other than the work state, the operation state of the hydraulic excavator  10 , and the reaction from the work-target object may be used as the learning data, and as the input data during the automatic control of the hydraulic excavator  10 . 
     [Operation] 
     Next, operation of the hydraulic excavator  10  with learning function configured as described above, is described. Below, the operation of the hydraulic excavator  10  is described taking a case where the hydraulic excavator  10  performs the digging work as the given work, as one example. 
       FIG. 9  is a schematic view illustrating a situation of a constructing work performed by the hydraulic excavator  10  with learning function. 
     Referring to  FIG. 9 , the first imaging device  311  is placed above a site at which a hole  131  is planned to be dug. The first imaging device  311  is, for example, attached to a suitable supporting member installed on the ground. The first imaging device  311  is, for example, wirelessly and data-communicably connected to the arithmetic unit (the learning module  118 ) provided to the swiveling body  15  of the hydraulic excavator  10 . The first imaging device  311  is placed, for example, above the center part of the hole  131  such that the optical axis  321  is oriented toward the center part of the hole  131 . 
     Next, the operation mode of the hydraulic excavator  10  is switched to the learning mode. Then, first, the hydraulic excavator  10  is set to an initial state. In this initial state, for example, the hydraulic excavator  10  is located at an installation place suitable for digging the hole  131 , and takes an initial posture (e.g., a posture illustrated in  FIG. 9 ). The installation place is set to, for example, a place where the hydraulic excavator  10  can dig the hole  131  by scooping earth and sand by the bucket  18 , and dispose the scooped earth and sand inside the bucket  18  at an earth-and-sand disposal area, without traveling of the hydraulic excavator  10 . Here, the installation place is assumed to be set to an intermediate position between the site where the hole  131  is to be dug, and the earth-and-sand disposal area. Moreover, the first imaging device  311  is assumed to be a 3D camera. 
     &lt;Digging Work&gt; 
     Next, the highly-skilled operator performs the digging work while manipulating the hydraulic excavator  10 . The operator performs the digging work schematically as described below. 
     First, the bucket  18  is lowered from a descending position above the hole  131  so as to be stuck into the ground (including the ground inside the hole  131 ) at the site where the hole  131  is to be dug (sticking operation). In this sticking operation, the operator manipulates the bucket  18  so that a claw of the bucket  18  at its tip end is oriented downward before lowering the bucket  18 . Next, the stuck bucket  18  is pivoted to a closer side while being pushed onto the ground, so as to scoop earth and sand (scooping operation). Next, the bucket  18  is moved to the descending position to be lifted from the hole  131  (lifting operation). Next, the swiveling body  15  is swiveled until the booms  16  and  17  are oriented toward the earth-and-sand disposal area (forward swiveling operation). Next, the bucket  18  is pivoted to a farther side to dispose the earth and sand inside the bucket  18  at the earth-and-sand disposal area (earth-and-sand disposing operation). Next, the swiveling body  15  is reversely swiveled so that the bucket  18  is located at the descending position (reverse swiveling operation). After this, the series of operation is repeated, and the digging work is finished when the given hole  131  has a given planar shape, and a given depth. 
     &lt;Information for Determining Next Manipulation&gt; 
     During the series of operation, the operator visually confirms the state of the digging, and intuits how the bucket  18 , the boom, and the driver&#39;s seat (the swiveling body  15 ) are currently to be operated, through visual confirmation of the postures of the bucket  18  and the booms  16  and  17 , and through the current manipulated positions of the manipulation levers of the manipulation devices  51 ,  54 ,  57 , and  71 . Moreover, when the bucket  18  acts on the ground (digs the ground, rakes the earth, etc.), the operator determines, by sensing the reaction, whether or not the intended work (action) is performed. Then, the operator determines the next manipulation instantly considering these. Here, the reaction is, for example, the inclination and vibration (including the excitation force, the acceleration, and the angular acceleration) at the driver&#39;s seat. Moreover, when the operator determines the next manipulation, he/she puts importance on the state (e.g., a speed and noise) of the engine  26  which is the driving source. 
     Here, the state of the digging is one example of the information indicating the work state. The manipulated position is one example of the information indicating the operation state of the hydraulic excavator  10 . The inclination and vibration at the driver&#39;s seat (the swiveling body  15 ) is one example of the reaction from the ground. The state of the engine (e.g., the speed and noise) is information indicating the state of the driving source, and thus, indicating the operation state of the hydraulic excavator  10 . 
     &lt;Data Acquisition for Learning&gt; 
     Meanwhile, during the series of operation, the data for learning is acquired as described below. 
     The first imaging device  311  images the state of the digging work performed by the bucket  18 , the image processing module  312  applies the image processing to the captured image to generate the data indicative of the state of the digging work as the work-state data  212 , and the learning data memory  115  (accurately, the estimation-basic-data memory  117 ) stores the generated work-state data  212 . 
     In detail, during the operation from the sticking operation to the lifting operation, the first imaging device  311  mainly images the arm  17  and the bucket  18  as well as the hole  131 , and during the operation from the forward swiveling operation to the reverse swiveling operation, the first imaging device  311  mainly images the hole  131 . The image processing module  312  applies, for example, well-known image processing (e.g., edge processing) to the captured image to discriminate between the area of the arm  17  and the bucket  18  and the area of the hole  131 , and generates data of the planar shape of the hole  131  and the depth at the center part. Particularly, in the image captured during the earth-and-sand disposing operation, the area of the arm  17  and the bucket  18  does not exist, but only the area of the hole  131  exists. Therefore, in the work-state data  212  during the earth-and-sand disposing operation, the data of the planar shape of the hole  131  and the depth at the center part is accurately identified. 
     When the operator operates the acceleration device  50 , the arm manipulation device  51 , the swivel manipulation device  54 , the bucket manipulation device  57 , and the boom manipulation device  71 , the learning data memory  115  (the command data memory  116  and the estimation-basic-data memory  117 ) stores the command data  211  of the operation command  201  corresponding to each operation. During the series of operation described above, depending on a combination of the command data  211  each corresponding to the operation of the acceleration device  50 , the arm manipulation device  51 , the swivel manipulation device  54 , the bucket manipulation device  57 , and the boom manipulation device  71 , the operation to be performed by the bucket  18 , the boom, and the driver&#39;s seat (swiveling body  15 ) at each time point is identified. 
     The microphone  313  receives the noise of the engine  26  during the series of operation described above. This received noise data (operation-state data  213 ) is stored in the learning data memory  115  (accurately, the estimation-basic-data memory  117 ). 
     Particularly, during the scooping operation, for example, the operator causes the bucket  18  to pivot by increasing the output of the engine. Therefore, the earth and sand of the ground of the hole  131  can be appropriately scooped. Accordingly, during the scooping operation, a magnitude of the increased output of the engine can be identified based on a magnitude of the received noise data. 
     During the series of operation described above, the gyroscope  314  detects the inclination and vibration (including the excitation force, the acceleration, and the angular acceleration) of the swiveling body  15  (driver&#39;s seat) or the body part  102 , as the reaction from the ground of the hole  131 . This reaction data  214  is stored in the learning data memory  115  (accurately, the estimation-basic-data memory  117 ). 
     Particularly, during the sticking operation, the reaction from the ground when the bucket  18  is stuck into the ground indicates a hardness of the ground. Therefore, during the sticking operation, the hardness of the ground where the bucket  18  is stuck, is identified based on a magnitude of the reaction data. 
     As described above, in this embodiment, the data capable of identifying the information suitable for determining the next manipulation is acquired as the learning data. 
     &lt;Machine Learning&gt; 
     After the operation for learning is finished, the learning data stored in the learning data memory is used for the machine learning by the neural network. Here, as described above, when the time-series data Pdi of the learning data at each sampling time point ti is inputted, the past time-series data Pdi−1 to Pdi−n (“n” is a given positive number) is inputted. 
     &lt;Automatic Control&gt; 
     The hydraulic excavator  10 , after finished the machine learning, is set to the initial state described above. Next, the operation mode of the hydraulic excavator  10  is switched to the automatic control mode. Then, the automatic control of the hydraulic excavator  10  starts. During the automatic control, the estimation basic data Pd (the work-state data  212 , the command data  211 , the operation-state data (received noise data)  213 , and the reaction data  214 ) is inputted into the learning module  118  (accurately, the data inputting module  402  of the neural network), and is also stored in the estimation-basic-data memory  117 . Then, the data inputting module  402  inputs, at each sampling time point tj, the current estimation basic data Pdj together with the past estimation basic data Pdj−1 to Pdj−n, among the time-series data Pd 0  to Pdj−1 stored in the estimation-basic-data memory  117 . Accordingly, the learning module  118  outputs the estimated operation command Pf. 
     In this manner, the hydraulic excavator  10  is automatically controlled so that the digging work is performed similarly to the case when the hydraulic excavator  10  is manipulated by the highly-skilled operator. 
     As described above, according to Embodiment 1, when the highly-skilled operator manipulates the construction machinery  100  to perform the given work during the learning, the command data  211  corresponding to the operation command  201  according to the manipulation, and the estimation basic data including the work-state data  212  of the working part  104 , the operation-state data  213  of the operating part  103 , and the reaction data  214  received by the operating part  103  or the body part  102  from the work-target object due to the work performed by the working part  104 , are stored in the learning data memory  115 . Then, the learning module  118  receives the input of the estimation basic data stored in the learning data memory  115 , and accordingly, the machine learning of the learning data is performed to output the estimated operation command. Therefore, the manipulation by the highly-skilled operator can be learned to be outputted as the estimated operation command Pf. Then, during the automatic operation, when the estimation basic data Pd is inputted into the learning module  118 , the learning module  118  outputs the estimated operation command Pf. Then, the hydraulic drive system  1  drives the operating part  103  based on the estimated operation command Pf. Consequently, the manipulation by the highly-skilled operator is learned, and, as a result of the learning, the work is automatically performed based on the estimated operation command Pf outputted from the learning module  118 . Therefore, the construction machinery  100  can be provided, which is capable of learning the work performed by the construction machinery  100  through the manipulation by the human, and automatically performing the learned work. 
     Embodiment 2 
     Embodiment 2 of the present disclosure is the hydraulic excavator  10  of Embodiment 1, in which the operation-state detecting part  113  is further provided with a second imaging device  331  and an image processing module  333 . 
       FIG. 10  is a side view illustrating a configuration of hardware of the hydraulic excavator  10  with learning function according to Embodiment 2.  FIG. 11  is a functional block diagram illustrating a configuration of a control system of the hydraulic excavator  10  with learning function illustrated in  FIG. 10 . 
     Referring to  FIGS. 10 and 11 , in the hydraulic excavator  10  of this embodiment, the operation-state detecting part  113  is further provided with the second imaging device  331  and the image processing module  333 . The other configurations are the same as those of the hydraulic excavator  10  of Embodiment 1. 
     The second imaging device  331  entirely images the hydraulic excavator  10 . The image captured by the second imaging device  331  is applied with image processing by the processing part  333  so as to obtain data indicative of the posture of the hydraulic excavator  10 , and is outputted from the image processing module  333  as the operation-state data  213 . An optical axis  332  of the second imaging device  331  is oriented toward the hydraulic excavator  10 . 
     The second imaging device  331  is comprised of, for example, an ordinal digital camera. The second imaging device  331  is, for example, fixed via a suitable supporting member to a fixed object (e.g., the ground) separated from the vehicle of the hydraulic excavator  10 , or mounted on a drone. 
     For example, the image processing module  333  applies the image processing to the image captured by the second imaging device  331  to extract a contour of the hydraulic excavator  10 , and outputs this contour data as the operation-state data  214 . The contour data is postural data which can identify the posture of the operating part  103  of the hydraulic excavator  10 . 
     According to Embodiment 2, during the series of operation described in Embodiment 1, the posture of the hydraulic excavator  10  is identified based on the contour of the hydraulic excavator  10  in the operation-state data  214 . Therefore, the efficiency of the machine learning by the learning module  118  improves, and the estimated operation command Pf outputted from the learning module  118  during the automatic control becomes more appropriate. 
     Embodiment 3 
     Embodiment 3 of the present disclosure exemplarily describes the hydraulic excavator  10  of Embodiment 1, in which the operation-state detecting part  113  is further provided with a sensor part  341 . 
       FIG. 12  is a side view illustrating a configuration of hardware of the hydraulic excavator  10  with learning function according to Embodiment 3.  FIG. 13  is a functional block diagram illustrating a configuration of a control system of the hydraulic excavator  10  with learning function illustrated in  FIG. 12 . 
     Referring to  FIG. 13 , in the hydraulic excavator  10  of this embodiment, the operation-state detecting part  113  is further provided with the sensor part  341 . The other configurations are the same as those of the hydraulic excavator  10  of Embodiment 1. The sensor part  341  is comprised of sensors S 1  to S 4 . 
     Referring to  FIG. 12 , the hydraulic excavator  10  is provided with the sensors S 1  to S 4 . In detail, the swiveling body  15  is provided with the sensor S 1  which detects a rotational angle of the swiveling body  15  about the rotary axis A 1 . The boom  16  is provided at its base-end part with the sensor S 2  which detects a rotational angle of the boom  16  about the rotary axis A 2 . The arm  17  is provided at its base-end part with the sensor S 3  which detects a rotational angle of the arm  17  about the rotary axis A 3 . The arm  17  is provided at its tip-end part with the sensor S 4  which detects a rotational angle of the bucket  18  about the rotary axis A 4 . 
     The sensor part  341  outputs the data of the rotational angles detected by the sensors S 1  to S 4  as the operation-state data  214 . A combination of the data of the rotational angles detected by the sensors S 1  to S 4 , is the postural data which can identify the posture of the operating part  103  of the hydraulic excavator  10 . 
     According to Embodiment 2, during the series of operation described in Embodiment 1, the posture of the hydraulic excavator  10  is identified based on the combination of the rotational angles in the operation-state data  214  detected by the sensors S 1  to S 4 . Therefore, the efficiency of the machine learning by the learning module  118  improves, and the estimated operation command Pf outputted from the learning module  118  during the automatic control becomes more appropriate. 
     Embodiment 4 
     {Outline} 
     First, an outline of skill-inheriting construction machinery of Embodiment 4 is described. 
     [Configuration] 
       FIG. 14  is a functional block diagram illustrating a configuration of a control system of skill-inheriting construction machinery according to Embodiment 4 of the present disclosure. In  FIG. 14 , each arrow indicates a flow of a command, a motive power, information, or data. Each solid-line arrow indicates a flow of a command or data during the operation of the operating part  103 , and each broken line indicates a flow of a command or data during the learning. Note that, as described later, when the past estimation basic data is used for the learning, the data flows as indicated by the broken lines even during the operation of the operating part. This similarly applies to  FIGS. 18, 11, and 13 . 
     Referring to  FIG. 14 , skill-inheriting construction machinery  200  of Embodiment 4 is provided with the manipulating part  101 , the body part  102 , the operating part  103 , the hydraulic drive system  105 , the work-state detecting part  112 , the operation-state detecting part  113 , the reaction detecting part  114 , and a control part  401 . 
     The control part  401  is provided with a basic-operation commanding module  2119 , an operation command generating module  2120 , an operation-correcting-command generating module  2110 , the learning data memory  115 , and the learning module  118 . The learning data memory  115  includes an operation-correcting-command memory  2116 , and the estimation-basic-data memory  117 . 
     In Embodiment 4, the construction machinery with learning function is the skill-inheriting construction machinery  200  having the control part  401 . The manipulating part  101  outputs, as the command  201 , a manual operation correcting command  403  according to the operation by the operator, and the hydraulic drive system  105  drives the operating part  103  based on a basic operation command  402 , an automatic operation correcting command  404 , and the manual operation correcting command  403 . The control part  401  is provided with the basic-operation commanding module  2119  which outputs the basic operation command  402  to cause the working part to take a basic movement by the operating part  103 , the operation-correcting-command generating module  2110  as the command data generating module  1101 , which generates an operation correcting command Pm by adding the manual operation correcting command  403  to the automatic operation correcting command  404 , the operation-correcting-command memory  2116  which is the command memory  1102  and stores the operation correcting command Pm in a time series, the estimation-basic-data memory  117 , and the learning module  118 . The learning module  118  performs machine learning of operation correcting command PM′ stored in the operation-correcting-command memory  2116 , by using the estimation basic data Pd′ stored in the estimation-basic-data memory  117 , and, after the machine learning, the learning module  118  receives the input of the estimation basic data Pd during the operation of the operating part  103 , and outputs the automatic operation correcting command  404  which is the estimated command  1103 . 
     Below, a configuration of the skill-inheriting construction machinery  200  is described in detail. 
     The “construction machinery” may be any work machine, as long as it can perform a construction work by the operating part moving the working part according to the manipulation by the operator. The “construction machinery” may be, for example, a hydraulic excavator, a bulldozer, a tractor excavator, a wheeled loader, a trencher, an excavator, a crane, a lift vehicle, etc. 
     The manipulating part  101  outputs the manual operation correcting command  403  according to the operation by the operator. 
     The body part  102  is coupled to the operating part  103 . 
     The operating part  103  has the working part  104  which performs the work, and moves the working part  104  to perform the work. Here, the phrase “moves the working part  104 ” means “cause the working part  104  to operate and move.” 
     The hydraulic drive system  105  is provided over the body part  102  and the operating part  103 . The hydraulic drive system  105  outputs a drive force  202  based on the operation command  201  outputted from the operation command generating module  2120 , thus driving the operating part  103 . 
     The work-state detecting part  112  detects the state of the work performed by the working part  104 , and outputs the detected work state as the work-state data  212 . 
     The operation-state detecting part  113  detects the state of the operation of the operating part  103 , and outputs the detected operation state as the operation-state data  213 . 
     Here, during machine learning (described later) by the learning module  118 , data used for estimating the operation correcting command which is the target of the learning, is referred to as the “estimation basic data.” The estimation basic data includes the work-state data  212 , the operation-state data  213 , and the reaction data  214 . For convenience, the reference character “Pd’” is given to the estimation basic data used for the learning, and the reference character “Pd” is given to the estimation basic data used during the operation of the operating part  103 . Note that the estimation basic data Pd and Pd′ may include data other than the work-state data  212 , the operation-state data  213 , and the reaction data  214 . 
     The reaction detecting part  114  detects the reaction received by the operating part  103  or the body part  102  from the work-target object due to the work performed by the working part  104 , and outputs the detected reaction as the reaction data  214 . 
     The basic-operation commanding module  2119  outputs the basic operation command  402  for the operating part  103  to cause the working part  104  to take the basic movement. 
     The operation-correcting-command generating module  2110  generates the operation correcting command Pm by adding the manual operation correcting command  403  to the automatic operation correcting command  404 . 
     The operation-correcting-command memory  2116  stores the operation correcting command Pm in a time series. The estimation-basic-data memory  117  stores, in a time series, the estimation basic data including the work-state data  212 , the operation-state data  213 , and the reaction data  214 . 
     The learning module  118  is a learning model which performs the machine learning. The learning model is, for example, a neural network, a regression model, a tree model, a Bayesian model, a time-series model, a cluster model, an ensemble learning model, etc. In this embodiment, the learning model is the neural network. The mode of the learning may be supervised or unsupervised learning. 
     For example, in case of the supervised learning, the learning module  118  reads, during the learning, the operation correcting command Pm stored in the operation-correcting-command memory  2116  as operation correcting command Pm′ for learning, and also reads the estimation basic data Pd stored in the estimation-basic-data memory  117  as the estimation basic data Pd′ for learning. Then, learning data is created using the operation correcting command Pm′ for learning as teacher data pn, and the estimation basic data Pd′ for learning as input data. Then, the estimation basic data Pd′, which is the input data, is inputted into the machine learning model (e.g., the neural network), a difference between an output Pn and the teacher data pm is evaluated, and the evaluation is fed back to the machine learning model. Accordingly, the machine learning model can carry out the machine learning of the learning data. In other words, the machine learning model learns the operation correcting command Pm′ by using the estimation basic data Pd′. 
     After the machine learning is finished, the learning module  118  outputs the output of the machine learning model to outside as the automatic operation correcting command  404 . In detail, during the operation of the operating part  103 , when the machine-learning model of the learning module  118  receives the input of the estimation basic data Pd, it outputs the automatic operation correcting command  404 , which is the estimated command of the operation correcting command Pm′ which was the learning target. 
     The operation command generating module  2120  generates the operation command  201  by adding the automatic operation correcting command  404 , and the manual operation correcting command  403  outputted from the manipulating part  101 , to the basic operation command  402 . Here, the basic operation command, the automatic operation correcting command, and the manual operation correcting command are numerical data indicative of opening of the hydraulic control valves provided to the hydraulic drive system  105 , and thus, addition and subtraction of these data is possible. 
     The hydraulic drive system  105  drives the operating part  103  based on the operation command  201 . 
     [Operation] 
     The given work is, for example, a comparatively simple routine work. The routine work may be, for example, a digging work, a ground leveling work, and a rolling compaction work. It is assumed that such a given work is repeatedly performed a plurality of times by the skill-inheriting construction machinery  200  while a work position is changed. In this case, between the given works performed twice one after another, the operation correcting command Pm′ in the earlier given work is learned by the learning module  118 , and during the latter given work, when the learning module  118  receives the input of the estimation basic data Pd, it outputs the automatic operation correcting command  404  which reflects the result of the learning. 
     In detail, during the earlier given work, the highly-skilled operator manipulates the skill-inheriting construction machinery  200  to perform the given work while correcting as necessary the movement of the working part  104  based on the basic operation command  402  and the automatic operation correcting command  404 . 
     Then, the estimation basic data Pd including the work-state data  212  indicative of the work state by the working part  104 , the operation-state data  213  indicative of the operation state of the operating part  103 , and the reaction data  214  indicative of the reaction received by the operating part from the work-target object due to the work performed by the working part, is stored in the estimation-basic-data memory  17  in a time series, and the operation correcting command Pm adding the manual operation correcting command  403  to the automatic operation correcting command  404  is stored in the operation-correcting-command memory  2116  in a time series. Then, during the learning after that, the learning module  118  reads the estimation basic data Pd′ from the estimation-basic-data memory  117 , and uses it to perform the machine learning of the operation correcting command Pm′ read from the operation-correcting-command memory  2116 . Therefore, the correcting manipulation by the highly-skilled operator can be learned to be outputted as the automatic operation correcting command  404 . Then, during the latter given work, when the learning module  118  receives the input of the estimation basic data Pd, it outputs the automatic operation correcting command  404 . Then, the hydraulic drive system  105  drives the operating part  103  while reflecting the basic operation command  402  and the automatic operation correcting command  404 . Consequently, the work reflecting the automatic operation correcting command  404  as a result of the learning of the correcting manipulation by the highly-skilled operator, is performed. Therefore, the construction machinery  200  can be provided, which is capable of learning the work performed by the skill-inheriting construction machinery  200  while the operator corrects the basic movement of the working part  104  caused by the basic-operation commanding module  2119 , and automatically performing the learned work. 
     Accordingly, the skill-inheriting construction machinery  200  can be provided, which is capable of taking over the skill of the highly-skilled operator in the construction industry, and achieving the automation of the given work in the short period of time. 
     {Concrete Configuration} 
     Next, a concrete configuration of the skill-inheriting construction machinery  200  is described, taking a hydraulic excavator  20  as one example of the construction machinery. 
     [Configuration of Hardware] 
     &lt;Entire Configuration&gt; 
     First, the entire configuration of the skill-inheriting hydraulic excavator  20  is described. The entire configuration of the skill-inheriting hydraulic excavator  20  is basically the same as that of the hydraulic excavator  10  with learning function of Embodiment 1, but is partially different. 
       FIG. 15  is a side view illustrating a configuration of hardware of the skill-inheriting hydraulic excavator  20 , which is one example of the skill-inheriting construction machinery. 
     The skill-inheriting hydraulic excavator (hereinafter, may simply be referred to as a “hydraulic excavator”)  20  is provided with the body part  102 . The body part  102  is provided with the traveling body  19 . The traveling body  19  is comprised of, for example, a vehicle traveling device provided with a continuous track (caterpillar). 
     The swiveling body  15  is provided on the body part  102  so as to be swivable about the vertical first rotary axis A 1 . The swiveling body  15  is provided with the driver&#39;s seat (not illustrated), and the manipulating part  101  is provided to the driver&#39;s seat (see  FIG. 18 ). Note that, although not illustrated in the manipulating part  101  of  FIG. 18 , a travel-manipulating device which operates the traveling body  19  is provided to the driver&#39;s seat. The swiveling body  15  is further provided with the swiveling motor  14  which causes the swiveling body  15  to swivel. The swiveling motor  14  is comprised of a hydraulic motor. The swiveling body  15  is also provided with the engine  26  (see  FIG. 18 ) for traveling. The engine  26  drives the pump part  107  (see  FIG. 18 ) of the hydraulic drive system  1  during the work. 
     The base-end part of the boom  16  is coupled to the swiveling body  15  so as to be pivotable about the horizontal second rotary axis A 2 . The tip-end part and the base-end part of the boom cylinder  11  are rotatably coupled to the base-end part of the boom  16  and the swiveling body  15 , respectively, and the boom  16  swings centering on the second rotary axis A 2  according to the extension and contraction of the boom cylinder  11 . 
     The base-end part of the arm  17  is coupled to the tip-end part of the boom  16  so as to be pivotable about the horizontal third rotary axis A 3 . The tip-end part and the base-end part of the arm cylinder  12  are rotatably coupled to the base-end part of the arm  17  and the tip-end part of the boom  16 , respectively, and the arm  17  swings centering on the third rotary axis A 3  according to the extension and contraction of the arm cylinder  12 . 
     The base-end part of the bucket  18  is coupled to the tip-end part of the arm  17  so as to be pivotable about the horizontal fourth rotary axis A 4 . The tip-end part and the base-end part of the bucket cylinder  13  are rotatably coupled to the base-end part of the bucket  18  and the tip-end part of the arm  17 , respectively, and the bucket  18  pivots centering on the fourth rotary axis A 4  according to the extension and contraction of the bucket cylinder  13 . The bucket  18  is one example of an attachment, and other attachments may be used. 
     The boom  16 , the arm  17 , and the bucket  18  constitute a front-work device. Moreover, the bucket  18  constitutes the working part  104 , and the swiveling body  15  and the front-work device (the boom  16 , the arm  17 , and the bucket  18 ) constitute the operating part  103 . 
     In addition to the above structures, the hydraulic excavator  20  is provided with left-and-right pair of hydraulic traveling motors (not illustrated). 
     As will be described later, an operation-mode-switching manipulation part (not illustrated) which switches the operation mode of the hydraulic excavator  20  between a manual mode and a semi-automatic mode, is provided to the driver&#39;s seat of the skill-inheriting hydraulic excavator  20 . The operator carries out the given work by operating the operation-mode-switching manipulation part to set the operation mode to the manual mode, then, operating the manipulating part  101  (including the travel-manipulating device (not illustrated)) to position the hydraulic excavator at a desired work site for the given work, then, operating the operation-mode-switching manipulation part to set the operation mode to the semi-automatic mode, and then, swiveling the swiveling body  15 , and changing the postures of the booms  16  and  17 , and rotary-driving the bucket  18 . 
     The skill-inheriting hydraulic excavator  20  is further provided with the first imaging device  311 . The first imaging device  311  images the state of the work performed by the bucket  18 . The image captured by the first imaging device  311  is applied with image processing by the image processing module  312  (see  FIG. 18 ) described later, so that the data indicative of the work state can be obtained, and is outputted from the image processing module  312  as the work-state data  212 . The optical axis  321  of the first imaging device  311  is oriented toward the work-target object. 
     The first imaging device  311  is comprised of, for example, a three-dimensional (3D) camera, a camera with a depth sensor, etc. The first imaging device  311  is, for example, fixed via a suitable support member to the body part  102 , fixed via a suitable supporting member to a fixed object (e.g., the ground) separated from the vehicle of the hydraulic excavator  20 , or mounted on a drone. 
     &lt;Hydraulic Drive System  1 &gt; 
     Next, the hydraulic drive system  1  which causes the hydraulic excavator  20  to operate, is described. 
       FIG. 16  is a hydraulic circuit diagram illustrating a main hydraulic circuit of the hydraulic drive system  1  of the skill-inheriting hydraulic excavator  20 .  FIG. 17  is a hydraulic circuit diagram illustrating a hydraulic circuit of an operation system (operating-system hydraulic circuit) in the hydraulic drive system of the skill-inheriting hydraulic excavator  20 . The main hydraulic circuit and the operating-system hydraulic circuit are provided to the swiveling body  15 . 
     As described above, the hydraulic drive system  1  includes the boom cylinder  11 , the arm cylinder  12 , and the bucket cylinder  13  as hydraulic actuators, and also includes the swiveling motor  14  and the left-and-right pair of hydraulic traveling motors (not illustrated). 
     Referring to  FIG. 16 , the hydraulic drive system  1  includes the first main pump  21  and the second main pump  23 , which supply hydraulic oil to the actuators described above. Note that, in  FIG. 16 , illustration of the actuators other than the swiveling motor  14  is omitted for simplification of the drawing. 
     The first main pump  21  and the second main pump  23  are driven by the engine  26 . The engine  26  also drives the sub pump  25 . The first main pump  21 , the second main pump  23 , and the sub pump  25  constitute the pump part  107  (see  FIG. 18 ). The output of the engine  26  is adjusted by the acceleration device  50  (see  FIG. 18 ). The acceleration device  50  is provided with, for example, an accelerator pedal, and outputs an acceleration correcting command  75  which is an electrical command corresponding to an amount of depression of the accelerator pedal. Then, an engine controlling device (not illustrated) controls the output (speed) of the engine  26  based on an individual operation command  75 ′ corresponding to the acceleration correcting command  75 . 
     The first main pump  21  and the second main pump  23  are, for example, variable displacement pumps which discharge hydraulic oil in an amount corresponding to a tilt angle. Here, the first main pump  21  and the second main pump  23  are swash plate pumps which define their tilt angles by angles of swash plates. However, the first main pump  21  and the second main pump  23  may be bent axis pumps which define their tilt angles by angles each formed between a drive shaft and a cylinder block. 
     The amount of discharge Q 1  of the first main pump  21 , and the amount of discharge Q 2  of the second main pump  23  are controlled in an electric positive control method. In detail, the tilt angle of the first main pump  21  is adjusted by the first flow-amount adjusting device  22 , and the tilt angle of the second main pump  23  is adjusted by the second flow-amount adjusting device  24 . The sub pump  25  is connected to the first flow-amount adjusting device  22  and the second flow-amount adjusting device  24  via the sub bleed line  37 . The sub pump  25  functions as the drive source of the first flow-amount adjusting device  22  and the second flow-amount adjusting device  24 . Details of the first flow-amount adjusting device  22  and the second flow-amount adjusting device  24  will be described later. 
     The first center bleed line  31  is extended from the first main pump  21  to a tank. The plurality of control valves including the first arm control valve  41  and the swivel control valve  43  (only the first arm control valve  41  and the swivel control valve  43  are illustrated) are provided on the first center bleed line  31 . The control valves are connected to the first main pump  21  through the pump lines  32 , respectively. That is, the control valves on the first center bleed line  31  are parallelly connected to the first main pump  21 . Moreover, the control valves are connected to the tank through the tank lines  33 , respectively. 
     Similarly, the second center bleed line  34  is extended from the second main pump  23  to a tank. The plurality of control valves including the second arm control valve  42  and the bucket control valve  44  (only the second arm control valve  42  and the bucket control valve  44  are illustrated) are provided on the second center bleed line  34 . The control valves are connected to the second main pump  23  through the pump lines  35 , respectively. That is, the control valves on the second center bleed line  34  are parallelly connected to the second main pump  23 . Moreover, the control valves are connected to the tank through the tank lines  36 , respectively. 
     The first arm control valve  41  controls, together with the second arm control valve  42 , the supply and discharge of the hydraulic oil to the arm cylinder  12 . That is, the hydraulic oil is supplied to the arm cylinder  12  from the first main pump  21  through the first arm control valve  41 , as well as from the second main pump  23  through the second arm control valve  42 . The first arm control valve  41  and the second arm control valve  42  constitute the arm control valve  40  (see  FIG. 17 ). 
     The swivel control valve  43  controls the supply and discharge of hydraulic oil to the swiveling motor  14 . That is, the hydraulic oil is supplied to the swiveling motor  14  from the first main pump  21  through the swivel control valve  43 . In detail, the swiveling motor  14  is connected to the swivel control valve  43  through the pair of supply-and-discharge lines  61  and  62 . The bypass lines  63  are branched from the supply-and-discharge lines  61  and  62 , respectively, and are connected to a tank. Each bypass line  63  is provided with the relief valve  64 . Moreover, the supply-and-discharge lines  61  and  62  are connected to the tank through the pair of makeup lines  65 , respectively. Each makeup line  65  is provided with the check valve  66  which allows the flow from the tank to the supply-and-discharge line ( 61  or  62 ), but prohibits the backflow. 
     The bucket control valve  44  controls the supply and discharge of hydraulic oil to the bucket cylinder  13 . That is, the hydraulic oil is supplied to the bucket cylinder  13  from the second main pump  23  through the bucket control valve  44 . 
     Although not illustrated in  FIG. 16 , the control valves on the second center bleed line  34  include the first boom control valve  45  (see  FIG. 17 ), and the control valves on the first center bleed line  31  include the second boom control valve  46  (see  FIG. 17 ). The second boom control valve  46  is the valve dedicated for the operation of lifting the boom. That is, during the lifting of the boom, hydraulic oil is supplied to the boom cylinder  11  through the first boom control valve  45  and the second boom control valve, and during the lowering of the boom, hydraulic oil is supplied through only the first boom control valve  45 . 
     Moreover, in this embodiment, each of the control valves  41  to  46  is comprised of an electromagnetic control valve. 
     Referring to  FIG. 17 , the boom control valve  47  (the first boom control valve  45  and the second boom control valve) is operated by the boom manipulation device  71 . The arm control valve  40  (the first arm control valve  41  and the second arm control valve  42 ) is operated by the arm manipulation device  51 . The swivel control valve  43  is operated by the swivel manipulation device  54 . The bucket control valve  44  is operated by the bucket manipulation device  57 . 
     In detail, the arm manipulation device  51 , the swivel manipulation device  54 , the bucket manipulation device  57 , and the boom manipulation device  71  include manipulation levers, and output a pair of individual manual operation correcting commands ( 52 ,  53 ), ( 55 ,  56 ), ( 58 ,  59 ), and ( 72 ,  73 ) which are electrical command signals corresponding to the tilt angles of the manipulation levers, respectively. These individual manual operation correcting commands ( 52 ,  53 ), ( 55 ,  56 ), ( 58 ,  59 ), and ( 72 ,  73 ), and the acceleration correcting command  75  (described later) outputted from the acceleration device  50  constitute the manual operation correcting command  403 . 
     The individual manual operation correcting commands  52  and  53  outputted from the arm manipulation device  51  are added, by the operation command generating module  2120 , with a pair of individual basic operation commands (not illustrated) corresponding to the arm manipulation device  51  in the basic operation command  402 , and a pair of individual automatic operation correcting commands (not illustrated) corresponding to the arm manipulation device  51  in the automatic operation correcting command  404 , to be a pair of individual operation commands  52 ′ and  53 ′ corresponding to the arm manipulation device  51 , and are inputted into a pair of solenoids of the first arm control valve  41  and a pair of solenoids of the second arm control valve  42 , respectively. 
     The individual manual operation correcting commands  55  and  56  outputted from the swivel manipulation device  54  are added, by the operation command generating module  2120 , with a pair of individual basic operation commands (not illustrated) corresponding to the swivel manipulation device  54  in the basic operation command  402 , and a pair of individual automatic operation correcting commands (not illustrated) corresponding to the swivel manipulation device  54  in the automatic operation correcting command  404 , to be a pair of individual operation commands  55 ′ and  56 ′ corresponding to the swivel manipulation device  54 , and are inputted into a pair of solenoids of the swivel control valve  43 , respectively. 
     The individual manual operation correcting commands  58  and  59  outputted from the bucket manipulation device  57  are added, by the operation command generating module  2120 , with a pair of individual basic operation commands (not illustrated) corresponding to the bucket manipulation device  57  in the basic operation command  402 , and a pair of individual automatic operation correcting commands (not illustrated) corresponding to the bucket manipulation device  57  in the automatic operation correcting command  404 , to be a pair of individual operation commands  58 ′ and  59 ′ corresponding to the bucket manipulation device  57 , and are inputted into a pair of solenoids of the bucket control valve  44 , respectively. 
     The individual manual operation correcting commands  72  and  73  outputted from the boom manipulation device  71  are added, by the operation command generating module  2120 , with a pair of individual basic operation commands (not illustrated) corresponding to the boom manipulation device  71  in the basic operation command  402 , and a pair of individual automatic operation correcting commands (not illustrated) corresponding to the boom manipulation device  71  in the automatic operation correcting command  404 , to be a pair of individual operation commands  72 ′ and  73 ′ corresponding to the boom manipulation device  71 , and are inputted into a pair of solenoids of the first boom control valve  45 , respectively. Moreover, the individual operation command  73 ′ is inputted into a solenoid of the second boom control valve  46  for lifting the boom, but the individual operation command is not inputted into a solenoid of the second boom control valve  46  for lowering the boom. Therefore, the second boom control valve  46  does not operate when the boom manipulation device  71  is operated to lower the boom. 
     Note that the acceleration command  75  outputted from the acceleration device  50  is added, by the operation command generating module  2120 , with an individual basic operation command (not illustrated) corresponding to the acceleration device  50  in the basic operation command  402 , and an individual automatic operation correcting command (not illustrated) corresponding to the acceleration device  50  in the automatic operation correcting command  404 , to be an individual operation command  75 ′ corresponding to the acceleration device  50 , and is inputted into the engine  26 . 
     The pairs of individual operation commands ( 52 ′,  53 ′), ( 55 ′,  56 ′), ( 58 ′,  59 ′), and ( 72 ′,  73 ′), and the individual operation command  75 ′ constitute the operation command  201 . 
     The first flow-amount adjusting device  22  and the second flow-amount adjusting device  24  described above are electrically controlled by the flow-amount controlling device  8 . For example, the flow-amount controlling device  8  has a memory (e.g., a ROM and a RAM) and a CPU, and the program stored in the ROM is executed by the CPU. The flow-amount controlling device  8  controls the first flow-amount adjusting device  22  and the second flow-amount adjusting device  24  so that the tilt angles of the first main pump  21  and/or the second main pump  23  increase as the pairs of individual operation commands ( 52 ′,  53 ′), ( 55 ′,  56 ′), ( 58 ′,  59 ′), and ( 72 ′,  73 ′) corresponding to the manipulation devices  51 ,  54 ,  57 , and  71  increase. For example, when the swiveling manipulation alone is performed, the flow-amount controlling device  8  controls the first flow-amount adjusting device  22  so that the tilt angle of the first main pump  21  increases as the pair of individual operation commands ( 55 ′,  56 ′) corresponding to the swivel manipulation device  54  increase. 
     [Configuration of Control System] 
     Next, a configuration of a control system of the skill-inheriting hydraulic excavator  20  is described. 
     &lt;Entire Configuration&gt; 
       FIG. 18  is a functional block diagram illustrating the configuration of the control system of the skill-inheriting hydraulic excavator  20 .  FIG. 18  illustrates the control system of the hydraulic excavator  20  related to the learning function of this embodiment. Therefore, the control system of the traveling body  19  unrelated to the learning function of this embodiment is omitted. Moreover, the skill-inheriting hydraulic excavator  20  has an overall-controlling module and an operation-mode-switching manipulation part (neither of them is illustrated). The overall-controlling module switches the operation mode of the skill-inheriting hydraulic excavator  20  between the manual mode and the semi-automatic mode, according to the operation of the operator to the operation-mode-switching manipulation part. 
     &lt;Manipulating Part&gt; 
     Referring to  FIG. 18 , the acceleration device  50 , the arm manipulation device  51 , the swivel manipulation device  54 , the bucket manipulation device  57 , and the boom manipulation device  71  constitute the manipulating part  101 . The manipulating part  101  is provided to the driver&#39;s seat of the swiveling body  15 . 
     &lt;Hydraulic Control&gt; 
     *Manual Mode* 
     In the manual mode, when the operator depresses the accelerator pedal of the acceleration device  50 , the acceleration device  50  outputs the acceleration correcting command  75  corresponding to the depressed amount of the accelerator pedal. Then, the engine  26  drives the pump part  107  at the output based on the individual operation command  75 ′ corresponding to the acceleration correcting command  75 . Then, the pump part  107  discharges hydraulic oil to/from a hydraulic circuit  106  in an amount corresponding to the output of the pump part  107 . 
     When the operator operates the manipulation lever of the swivel manipulation device  54 , the swivel manipulation device  54  outputs the pair of individual manual operation correcting commands  5  and  56  (swivel manual operation correcting command) corresponding to the tilt angle of the manipulation lever. Then, the swivel control valve  43  supplies and discharges hydraulic oil to/from the swiveling motor  14  based on the pair of individual operation commands  55 ′ and  56 ′ corresponding to the pair of individual manual operation correcting commands  55  and  56 . Then, the swiveling motor  14  causes the swiveling body  15  to swivel according to the supply and discharge of the hydraulic oil. 
     When the operator operates the manipulation lever of the boom manipulation device  71 , the boom manipulation device  71  outputs the pair of individual manual operation corrections  72  and  73 ′ (boom manual operation correcting command) corresponding to the tilt angle of the manipulation lever. Then, the boom control valve  47  supplies and discharges hydraulic oil to/from the boom cylinder  11  based on the pair of individual operation commands  72 ′ and  73 ′ corresponding to the pair of individual manual operation corrections  72  and  73 . Then, the boom cylinder  11  lifts and lowers the boom  16  according to the supply and discharge of the hydraulic oil. 
     When the operator operates the manipulation lever of the arm manipulation device  51 , the arm manipulation device  51  outputs the pair of individual manual operation correcting commands  52  and  53  (arm manual operation correcting operation command) corresponding to the tilt angle of the manipulation lever. Then, the arm control valve  40  supplies and discharges hydraulic oil to/from the arm cylinder  12  based on the pair of individual operation commands  52 ′ and  53 ′ corresponding to the pair of individual manual operation correcting commands  52  and  53 . Then, the arm cylinder  12  swings the arm  17  according to the supply and discharge of the hydraulic oil. 
     When the operator operates the manipulation lever of the bucket manipulation device  57 , the bucket manipulation device  57  outputs the pair of individual manual operation correcting commands  58  and  59  (bucket manual operation operation command) corresponding to the tilt angle of the manipulation lever. Then, the bucket control valve  44  supplies and discharges hydraulic oil to/from the bucket  18  based on the pair of individual operation commands  58 ′ and  59 ′ corresponding to the pair of individual manual operation correcting commands  58  and  59 . Then, the bucket cylinder  13  pivots the bucket  18  according to the supply and discharge of the hydraulic oil. 
     According to these manipulations, the work can be performed as intended by the operator. 
     *Semi-Automatic Mode* 
     In the semi-automatic mode, the operating part  103  ( 15  to  18 ) operates based on the basic operation command  402  outputted from the basic-operation commanding module  2119  and the automatic operation correcting command  404  outputted from the learning module  118 , and, when the operator operates the manipulating part  101  ( 50 ,  51 ,  54 ,  5 , and  71 ), the operation of the operating part  103  is corrected according to the manipulation by the operator. 
     &lt;Estimation Basic Data Detecting Part&gt; 
     As described above, the first imaging device  311  images the state of the work performed by the bucket  18 . Then, the image processing module  312  applies image processing to the image captured by the first imaging device  311  so as to generate data indicative of the state of the given work, and outputs it as the work-state data  212 . Therefore, the first imaging device  311  and the image processing module  312  constitute the work-state detecting part  112 . 
     The hydraulic excavator  20  is provided with the microphone  313 . The microphone  313  is installed, for example, near the engine  26  provided to the swiveling body  15 , and receives operation noise of the engine  26  and converts it to operation noise data so as to output it as the operation-state data  213 . The operation noise of the engine  26  increases as the output of the engine  26  increases, while it decreases as the output of the engine  26  decreases. Therefore, the operation noise data shows a driving-source state indicative of a state of the driving source of the pump part  107 , and thus, it is the operation-state data indicative of the operation state of the hydraulic excavator  20 . Accordingly, the microphone  313  constitutes a driving-source state detecting part, and thus, the operation-state detecting part  113 . 
     The swiveling body  15  or the body part  102  of the hydraulic excavator  20  is provided with the gyroscope  314 . The gyroscope  314  detects an inclination and vibration (including an excitation force, acceleration, and angular acceleration) of the swiveling body  15  or the body part  102 , converts them to inclination and vibration data, and outputs the inclination and vibration data as the reaction data  214 . For example, when the boom  16  and the arm  17  stick the bucket  18  into the ground, the swiveling body  15  and the body part  102  incline due to a reaction force from the ground, and the swiveling body  15  and the body part  102  vibrate by receiving the excitation force through the arm  17  and the boom  16  (the operating part  103 ). Therefore, the inclination and vibration data outputted from the gyroscope  314  indicates the reaction received by the operating part  103  and the body part  102 . Accordingly, the gyroscope  314  constitutes the reaction detecting part  114 . 
     &lt;Control Part&gt; 
     The control part  401 , the image processing module  312 , and the overall-controlling module (not illustrated) are, for example, comprised of an arithmetic unit including a processor and a memory. The operation-correcting-command generating module  2110 , the learning module  118 , the basic-operation commanding module  2119 , the operation command generating module  2120 , the overall-controlling module, and the image processing module  312  are functional blocks implemented by the processor executing a given program stored in the memory of the arithmetic unit. The learning data memory  115  is comprised of this memory. The arithmetic unit is comprised of, for example, a microcontroller, an MPU, an FPGA (Field Programmable Gate Array), a PLC (Programmable Logic Controller), etc. The control part  401 , the image processing module  312 , and the overall-controlling module (not illustrated) may be comprised of a sole arithmetic unit which executes a centralized control, or a plurality of arithmetic units which execute a distributed control. The sole arithmetic unit or the plurality of arithmetic units may be provided to the swiveling body  15  of the hydraulic excavator  20 , for example. 
     &lt;Basic-Operation Commanding Module  2119 &gt; 
     The basic-operation commanding module  2119  outputs the basic operation command  402  based on a control program for the given work stored in the memory of the arithmetic unit. 
     &lt;Operation Command Generating Module  2120 &gt; 
     Here, as described above, the operation command generating module  2120  generates the pair of individual operation commands  52 ′ and  53 ′ corresponding to the arm manipulation device  51 , by adding the pair of individual manual operation correcting commands  52  and  53  outputted from the arm manipulation device  51 , the pair of individual basic operation commands (not illustrated) corresponding to the arm manipulation device  51  in the basic operation command  402  outputted from the basic-operation commanding module  2119 , and the pair of individual automatic operation correcting commands (not illustrated) corresponding to the arm manipulation device  51  in the automatic operation correcting command  404  outputted from the learning module  118 . 
     Moreover, the operation command generating module  2120  generates the pair of individual operation commands  55 ′ and  56 ′ corresponding to the swivel manipulation device  54 , by adding the pair of individual manual operation correcting commands  55  and  56  outputted from the swivel manipulation device  54 , the pair of individual basic operation commands (not illustrated) corresponding to the swivel manipulation device  54  in the basic operation command  402 , and the pair of individual automatic operation correcting commands (not illustrated) corresponding to the swivel manipulation device  54  in the automatic operation correcting command  404 . 
     Moreover, the operation command generating module  2120  generates the pair of individual operation commands  58 ′ and  59 ′ corresponding to the bucket manipulation device  57 , by adding the pair of individual manual operation correcting commands  58  and  59  outputted from the bucket manipulation device  57 , the pair of individual basic operation commands (not illustrated) corresponding to the bucket manipulation device  57  in the basic operation command  402 , and the pair of individual automatic operation correcting commands (not illustrated) corresponding to the bucket manipulation device  57  in the automatic operation correcting command  404 . 
     Moreover, the operation command generating module  2120  generates the pair of individual operation commands  72 ′ and  73 ′ corresponding to the boom manipulation device  71 , by adding the pair of individual manual operation correcting commands  72  and  73  outputted from the boom manipulation device  71 , the pair of individual basic operation commands (not illustrated) corresponding to the boom manipulation device  71  in the basic operation command  402 , and the pair of individual automatic operation correcting commands (not illustrated) corresponding to the boom manipulation device  71  in the automatic operation correcting command  404 . 
     Moreover, the operation command generating module  2120  generates the individual operation command  75 ′ corresponding to the acceleration device  50 , by adding the individual manual operation correcting command  75  outputted from the acceleration device  50 , the individual basic operation command (not illustrated) corresponding to the acceleration device  50  in the basic operation command  402 , and the individual automatic operation correcting command (not illustrated) corresponding to the acceleration device  50  in the automatic operation correcting command  404 . 
     &lt;Operation-Correcting-Command Generating Module  2110 &gt; 
     Here, the operation-correcting-command generating module  2110  generates a pair of individual operation correcting commands (not illustrated) corresponding to the arm manipulation device  51  (hereinafter, referred to as an “arm operation correcting command”) by adding the pair of individual manual operation correcting commands  52  and  53  outputted from the arm manipulation device  51 , and the pair of individual automatic operation correcting commands (not illustrated) corresponding to the arm manipulation device  51  in the automatic operation correcting command  404  outputted from the learning module  118 . 
     Moreover, the operation-correcting-command generating module  2110  generates a pair of individual operation correcting commands (not illustrated) corresponding to the swivel manipulation device  54  (hereinafter, referred to as a “swivel operation correcting command”) by adding the pair of individual manual operation correcting commands  55  and  56  outputted from the swivel manipulation device  54 , and the pair of individual automatic operation correcting commands (not illustrated) corresponding to the swivel manipulation device  54  in the automatic operation correcting command  404 . 
     Moreover, the operation-correcting-command generating module  2110  generates a pair of individual operation correcting commands (not illustrated) corresponding to the bucket manipulation device  57  (hereinafter, referred to as a “bucket operation correcting command”) by adding the pair of individual manual operation correcting commands  58  and  59  outputted from the bucket manipulation device  57 , and the pair of individual automatic operation correcting commands (not illustrated) corresponding to the bucket manipulation device  57  in the automatic operation correcting command  404 . 
     Moreover, the operation-correcting-command generating module  2110  generates a pair of individual operation correcting commands (not illustrated) corresponding to the boom manipulation device  71  (hereinafter, referred to as a “boom operation correcting command”) by adding the pair of individual manual operation correcting commands  72  and  73  outputted from the boom manipulation device  71 , and the pair of individual automatic operation correcting commands (not illustrated) corresponding to the boom manipulation device  71  in the automatic operation correcting command  404 . 
     Moreover, the operation-correcting-command generating module  2110  generates an individual operation correcting command (not illustrated) corresponding to the acceleration device  50  (hereinafter, referred to as an “accelerating operation correcting command”) by adding the acceleration correcting command  75  outputted from the acceleration device  50 , and the individual automatic operation correcting command (not illustrated) corresponding to the acceleration device  50  in the automatic operation correcting command  404 . 
     These individual operation correcting commands constitute the operation correcting command Pm. 
     &lt;Learning Data Memory  115 &gt; 
     The operation-correcting-command memory  2116  of the learning data memory  115  stores the operation correcting command Pm in a time series. 
     The estimation-basic-data memory  117  of the learning data memory  115  stores, in a time series, the estimation basic data Pd including the work-state data  212  outputted from the image processing module  312 , the operation-state data  213  outputted from the microphone  313 , and the reaction data  214  outputted from the gyroscope  314 . 
     &lt;Learning Module  118 &gt; 
     As described above, during the learning, the learning module  118  executes the machine learning of the learning data comprised of the operation correcting command (hereinafter, referred to as an “operation correcting command for learning”) Pm′ read from the operation correcting command part  2116 , and the estimation basic data (hereinafter, referred to as an “estimation basic data for learning”) Pd′ read from the estimation-basic-data memory  17 . After the machine learning is finished, when the learning module  118  receives the input of the estimation basic data Pd during the operation of the operating part  103 , it outputs the automatic operation correcting command  404 . 
     Next, the configuration of the learning module  118  is described in detail.  FIG. 19  is a schematic view illustrating a cycle time of the operation of the skill-inheriting hydraulic excavator  20  illustrated in  FIG. 15 .  FIG. 20  is a schematic view illustrating time-series data of each of the operation correcting command Pm, the operation correcting command Pm′ for learning, the estimation basic data Pd, and the estimation basic data Pd′ for learning, in the skill-inheriting hydraulic excavator  20 .  FIG. 21  is a functional block diagram illustrating the configuration of the learning module  118 . 
     &lt;Temporal Relation Between Time-Series Data&gt; 
     First, a temporal relation between the time-series data is described. 
     Referring to  FIG. 19 , the hydraulic excavator  20  repeatedly performs the given work for a plurality of times. Although a time interval between the given works performed one after another is not fixed, in this embodiment, a period of time from a start of the earlier given work among the given works performed one after another, to immediately before the start of the latter given work is referred to as a “cycle time” for convenience. Moreover, the period of time between the earlier given work and the latter given work is allotted for time during which the learning module  118  executes the learning using the learning data (the operation correcting command Pm′ for learning and the estimation basic data Pd′ for learning) of the “earlier given work.” Below, the “period of time between the earlier given work and the latter given work” is referred to as a “learning time,” and the period of time during which the hydraulic excavator  20  performs the given work is referred to as an “operating time.” Moreover, the operation of the hydraulic excavator  20  during the “operating time” in each cycle time is referred to as a “-th operation.” 
     In  FIG. 19 , “operation” currently under progress is “this operation,” and “operation” before that is a “last operation.” In the operation currently under progress, time-series data Pm 0 , Pm 1 , Pm 2 , Pm 3  . . . Pmu (hereinafter, abbreviated as Pm 0  to Pmu) of the operation correcting command Pm, is acquired at a given sampling interval. Moreover, the time-series data Pd 0  to Pdu of the estimation basic data Pd is similarly acquired. Then, the time-series data Pm 0  to Pmu of the operation correcting command Pm acquired in the last operation becomes time-series data Pm 0 ′ to Pmu′ of the operation correcting command Pm′ for learning in this operation. Moreover, the time-series data Pd 0  to Pdu of the estimation basic data Pd acquired in the last operation becomes the time-series data Pd 0 ′ to Pdu′ of the estimation basic data Pd′ for learning in this operation. Below, a suffix number of the time-series data represents an order of sampling time point (intermittent time point). This means that the time-series data with the same suffix number are acquired at the same sampling time point. 
     &lt;Configuration of Learning Module  118 &gt; 
     Referring to  FIG. 21 , the learning module  118  is provided with, for example, a neural network  500 , a learning data and teacher data generating module  501 , a data inputting module  502 , and a learning evaluation module  503 . 
     The neural network  500  has an input layer, an intermediate layer, and an output layer. The number of neurons in each layer is set suitably. A well-known learning method may be applied to the learning by the neural network  500 . Therefore, it is briefly described here. Here, the neural network  500  is, for example, a recurrent neural network. The mode of learning is, for example, supervised learning. 
     The learning data and teacher data generating module  501  generates the time-series data pn 1  to pnu of the teacher data pn, based on the time-series data Pm 0 ′ to Pmu′ of the operation correcting command Pm′ for learning. Moreover, the time-series data Pd 0 ′ to Pdu−1′ of the learning data is generated based on the time-series data Pd 0 ′ to Pdu′ of the estimation basic data Pd′ for learning. 
     The data inputting module  502  sequentially inputs the time-series data Pd 0 ′ to Pdu−1′ of the learning data to each neuron of the input layer. Here, when the data inputting module  502  inputs the time-series data Pdi of the learning data at the certain sampling time point ti, the neural network  500  calculates an estimated operation correcting command Pni+1 at the next sampling time point ti+1 by the forward calculation. Then, the learning evaluation module  503  retrieves, among the time-series data pn 1  to pnu of the teacher data pn, the time-series data pni+1 at the next sampling time point ti+1, and, for example, calculates a sum of squared errors e 2  of the accelerating operation correcting command, the arm operation correcting command, the swivel operation correcting command, the bucket operation correcting command, and the boom operation correcting command, between the estimated operation correcting command Pni+1 and the time-series data pni+1 of the teacher data pn. Next, the learning evaluation module  503  updates weights of the neural network  500  by a backward calculation. The data inputting module  502  and the learning evaluation module  503  execute this processing for all of the time-series data Pd 0 ′ to Pdu−1′ of the learning data, and finish the learning when, for example, the sum of squared errors e 2  becomes below a given threshold in the processing for all of the time-series data Pd 0 ′ to Pdu−1′ of the learning data. 
     After the learning is finished, during the next operation of the hydraulic excavator  20 , the data inputting module  502  inputs the estimation basic operation data Pd 0  at the current sampling time point t 0 . Then, the neural network  500  outputs the estimated operation correcting command Pn 1  at the next sampling time point t 1  as the automatic operation correcting command  404 . Note that a suitable initial operation command is outputted as an initial value of the automatic operation correcting command  404 . 
     Therefore, the learning result of the neural network  500  (the learning module  118 ) is reflected on the operation of the hydraulic excavator  20 . 
     Note that, when the data inputting module  502  inputs the time-series data Pdi of the estimation basic data Pd′ for learning at a certain sampling time point ti, it may be the time-series data Pdi−1 to Pdi−n (“n” is a given positive number) prior to the sampling time point ti. In this case, during the operating time, the data inputting module  502  is required to similarly input at each sampling time point tj the past estimation basic data Pdj−1 to Pdj−n together with the current estimation basic data Pdj. Since the estimation basic data Pd 0  to Pdj−1 is stored in a time series in the estimation-basic-data memory  117  at each sampling time point tj, the data inputting module  502  reads it from the estimation-basic-data memory  117 , and generates the past estimation basic data Pdj−1 to Pdj−n. Accordingly, the learning efficiency of the neural network  500  improves. This is because, when the operator estimates the motion of the bucket  18 , he/she estimates the next motion of the bucket  18  considering not only the work state, the operation state of the hydraulic excavator  20 , and the reaction from the work-target object at the present moment, but also the series of work states, operation states of the hydraulic excavator  20 , and reactions from the work-target object in the past, and thus, the motion of the bucket  18  is estimated accurately. 
     Note that information on other than the work state, the operation state of the hydraulic excavator  20 , and the reaction from the work-target object may be used as the learning data, and the input data during the operating time of the hydraulic excavator  20 . 
     [Operation] 
     Next, operation of the skill-inheriting hydraulic excavator  20  configured as above, is described. Below, the operation of the hydraulic excavator  20  is described taking a case where the hydraulic excavator  20  performs the digging work as the given work, as one example. 
       FIG. 22  is a schematic view illustrating a situation of a constructing work performed by the skill-inheriting hydraulic excavator  20 . 
     Referring to  FIG. 22 , the first imaging device  311  is placed above a site at which the hole  131  is planned to be dug. The first imaging device  311  is, for example, attached to a suitable supporting member installed on the ground. The first imaging device  311  is, for example, wirelessly and data-communicably connected to the arithmetic unit (the learning module  118 ) provided to the swiveling body  15  of the hydraulic excavator  20 . The first imaging device  311  is placed, for example, above the center part of the hole  131  such that the optical axis  321  is oriented toward the center part of the hole  131 . 
     Next, the hydraulic excavator  20  is set to an initial state. In this initial state, for example, the hydraulic excavator  20  is located at an installation place suitable for digging the hole  131 , and takes an initial posture (e.g., a posture illustrated in  FIG. 22 ). The installation place is set to, for example, a place where the hydraulic excavator  20  can dig the hole  131  by scooping earth and sand by the bucket  18 , and dispose the scooped earth and sand inside the bucket  18  at an earth-and-sand disposal area, without traveling of the hydraulic excavator  10 . Here, the installation place is assumed to be set to an intermediate position between the site where the hole  131  is to be dug, and the earth-and-sand disposal area. Moreover, the first imaging device  311  is assumed to be a 3D camera. 
     Then, the operation mode of the hydraulic excavator  20  is switched to the semi-automatic mode. 
     &lt;Digging Work&gt; 
     *Case without Correcting Manipulation* 
     Next, the highly-skilled operator carries out the digging work while performing correcting manipulation of the hydraulic excavator  20 . Here, first, the operator does not perform the correcting manipulation in order to confirm the basic operation of the hydraulic excavator  20 . Below, this operation is referred to as an “initial operation.” In this initial operation, the digging work is performed by the basic-operation commanding module  2119  outputting the basic operation command  402  to operate the operating part  103 . Here, in a control program for a given work which defines (implements) the basic-operation commanding module  2119 , a geological feature (e.g., composition and a hardness of the ground) of the site to be dug, is assumed to be a given geological feature. The digging work is schematically performed as described below. 
     First, the basic-operation commanding module  2119  lowers the bucket  18  from a descending position above the hole  131  to stick it into the ground (including the ground inside the hole  131 ) at the site where the hole  131  is to be dug (sticking operation). In this sticking operation, the bucket  18  is operated so that a claw of the bucket  18  at its tip end is oriented downward before the bucket  18  is lowered. Next, the stuck bucket  18  is pivoted to a closer side while being pushed onto the ground, so as to scoop earth and sand (scooping operation). Next, the bucket  18  is moved to the descending position and lifted from the hole  131  (lifting operation). Next, the swiveling body  15  is swiveled until the boom  16  is oriented toward the earth-and-sand disposal area (forward swiveling operation). Next, the bucket  18  is pivoted to a farther side to dispose the earth and sand inside the bucket  18  at the earth-and-sand disposal area (earth-and-sand disposing operation). Next, the swiveling body  15  is reversely swiveled so that the bucket  18  is located at the descending position (reverse swiveling operation). After this, when the series of operation is repeated for a given times, the digging work is finished. 
     Meanwhile, although the learning data is acquired during this operation, and the machine learning is executed by the learning module  118  after finishing the given work, it will be described later in detail. 
     *Case with Correcting Manipulation in First Mode* 
     Next, the operator performs correcting manipulation in First Mode as one example of the correcting manipulation.  FIGS. 23( a ) to 23( c )  are cross-sectional views schematically illustrating a process in which the digging work performed by the hydraulic excavator  20  is improved through the correcting manipulation to dig down corners. In  FIGS. 23( a ) to 23( c ) , solid lines indicate a cross-sectional shape of the hole  131  actually formed, and two-dot lines indicate a given cross-sectional shape of the hole  131  assumed based on the control program for the given work. 
     As illustrated in  FIG. 23( a ) , a hole having a cross-sectional shape in which corners remain undug is formed through the work in the initial operation without the correcting manipulation. This is because the work of digging down the corners is too difficult to be sufficiently managed by the control program for the given work. 
     In this case, the operator operates the operation-mode-switching manipulation part to switch the operation mode to the manual mode, and manually manipulates the hydraulic excavator  20  so as to form the hole  131  having the given cross-sectional shape. Then, the operator moves the hydraulic excavator  20  to a site planned to be dug next. Below, operation after this is referred to as a “second operation.” 
     Referring to  FIG. 18 , the operator switches the operation mode of the hydraulic excavator  20  to the semi-automatic mode. Then, the hydraulic excavator  20  starts the basic operation described above. The operator operates the manipulating part  101  as necessary so as to correct the operation of the operating part  103  of the hydraulic excavator  20 . Particularly, when digging down the corners of the hole  131 , the basic operation is corrected almost entirely. 
     As described above, when the operator operates the manipulating part  101  (i.e., the acceleration device  50 , the arm manipulation device  51 , the swivel manipulation device  54 , the bucket manipulation device  57 , and the boom manipulation device  71 ), the manual operation correcting command  403  is outputted, and the basic operation command  402  is corrected based on the manual operation correcting command  403 . As a result, the basic operation of the operating part  103  is corrected according to the correction of the basic operation command  402 . Meanwhile, the manual operation correcting command  403  is added to the automatic operation correcting command  404  outputted from the learning module  118 , and thus, the operation correcting command Pm is generated. 
     &lt;Information for Determining Next Manipulation&gt; 
     During the correction, the operator visually confirms the state of the digging, and intuits how the bucket  18 , the arm  17 , the boom  16 , and the driver&#39;s seat (the swiveling body  15 ) are currently to be operated, through the visual confirmation of the postures of the bucket  18 , the arm  17 , and the boom  16 . Moreover, when the bucket  18  acts on the ground (digs the ground, rakes the earth, etc.), the operator determines, by sensing the reaction, whether or not the current work (action) is appropriate. Then, the operator determines the next manipulation instantly considering these. Here, the reaction is, for example, the inclination and vibration (including the excitation force, the acceleration, and the angular acceleration) at the driver&#39;s seat. Moreover, when the operator determines the next manipulation, he/she puts importance on the state (e.g., a speed and noise) of the engine  26  which is the driving source. 
     Here, the state of the digging is one example of the information indicating the work state. The postures of the bucket  18 , the arm  17 , and the boom  16  are one example of the information indicating the operation state of the hydraulic excavator  20 . The inclination and vibration at the driver&#39;s seat (the swiveling body  15 ) is one example of the reaction from the ground. The state of the engine  26  (e.g., the speed and the noise) is information indicating the state of the driving source, and thus, indicating the operation state of the hydraulic excavator  20 . Therefore, these information are preferably used as the estimation basic data Pd′ for the machine learning by the learning module  118 . 
     &lt;Data Acquisition for Learning&gt; 
     The operation-correcting-command memory  2116  of the learning data memory  115  stores the operation correcting command Pm corresponding to the manipulation described above. 
     On the other hand, during the series of operation for the given work described above, the estimation basic data Pd is acquired as described below. 
     The first imaging device  311  images the state of the digging work performed by the bucket  18 , the image processing module  312  applies the image processing to the captured image to generate the data indicative of the state of the digging work as the work-state data  212 , and the estimation-basic-data memory  117  of the learning data memory  115  stores the generated work-state data  212 . 
     In detail, during the operation from the sticking operation to the lifting operation, the first imaging device  311  mainly images the arm  17  and the bucket  18  as well as the hole  131 , and during the operation from the forward swiveling operation to the reverse swiveling operation, the first imaging device  311  mainly images the hole  131 . The image processing module  312  applies, for example, well-known image processing (e.g., edge processing) to the captured image to discriminate between the area of the arm  17  and the bucket  18  and the area of the hole  131 , and generates data of the planar shape of the hole  131  and the depth at the center part. Particularly, in the image captured during the earth-and-sand disposing operation, the area of the arm  17  and the bucket  18  does not exist, but only the area of the hole  131  exists. Therefore, in the work-state data  212  during the earth-and-sand disposing operation, the data of the planar shape of the hole  131  and the depth at the center part is accurately identified. 
     During the series of operation for the given work, the microphone  313  receives the noise of the engine  26 . This received noise data (the operation-state data  213 ) is stored in the estimation-basic-data memory  117 . 
     Particularly, during the scooping operation, for example, the operator causes the bucket  18  to pivot by increasing the output of the engine. Therefore, the earth and sand of the ground of the hole  131  can be appropriately scooped. Accordingly, during the scooping operation, a magnitude of the increased output of the engine can be identified based on a magnitude of the received noise data. 
     During the series of operation for the given work, the gyroscope  314  detects the inclination and vibration (including the excitation force, the acceleration, and the angular acceleration) of the swiveling body  15  (driver&#39;s seat) or the body part  102 , as the reaction from the ground of the hole  131 . This reaction data  214  is stored in the learning data memory  115  (accurately, the estimation-basic-data memory  117 ). 
     Particularly, during the sticking operation, the reaction from the ground when the bucket  18  is stuck into the ground, indicates a hardness of the ground. Therefore, during the sticking operation, the hardness of the ground where the bucket  18  is stuck, is identified based on a magnitude of the reaction data. 
     As described above, in this embodiment, the data capable of identifying the information suitable for determining the next manipulation is acquired as the learning data. 
     &lt;Machine Learning&gt; 
     After the given work by the hydraulic excavator  20  is finished, the learning data stored in the learning data memory  115  is used for the machine learning by the neural network  500 . Here, as described above, when the time-series data Pdi of the learning data at each sampling time point ti is inputted, the past time-series data Pdi−1 to Pdi−n (“n” is a given positive number) is inputted. 
     In the given work in the second operation, as illustrated in  FIG. 23( b ) , although the digging down of the corners is improved compared to the initial operation, it is still insufficient. The operator operates the operation-mode-switching manipulation part to switch the operation mode to the manual mode, and manually manipulates the hydraulic excavator  20  so as to form the hole  131  having the given cross-sectional shape. Then, the operator moves the hydraulic excavator  20  to a site planned to be dug next, and sets the hydraulic excavator  20  to the initial state described above. Below, operation after this is referred to as a “third operation.” 
     Referring to  FIG. 18 , the operator switches the operation mode of the hydraulic excavator  20  to the semi-automatic mode. Then, the hydraulic excavator  20  outputs the automatic operation correcting command  404  reflecting the learning result described above, and the basic operation command  402  is corrected based on the automatic operation correcting command  404 . Therefore, the operation of the operating part  103  is corrected so as to reflect the correcting manipulation in the second operation. 
     The operator further performs the correcting manipulation to the operation of the operating part  103  reflecting the correcting manipulation performed in the second operation, so that the cross-sectional shape of the hole  131  becomes the given shape. Accordingly, the manual operation correcting command  403  corresponding to the correcting manipulation is outputted, and the basic operation command  402  corrected based on the automatic operation correcting command  404  is further corrected based on the manual operation correcting command  403 . As a result, according to the given work in the third operation, as illustrated in  FIG. 23( c ) , the hole  131  having the given cross-sectional shape in which the corners are sufficiently dug down is formed. 
     Meanwhile, the manual operation correcting command  403  corresponding to the additional correcting manipulation is added to the automatic operation correcting command  404  which reflects the learning result of the correcting manipulation performed in the second operation, and thus, the operation correcting command Pm is generated. 
     During the learning after this, the learning module  118  executes the machine learning of the operation correcting command Pm, and the learning module  118  outputs the automatic operation correcting command  404  reflecting the operation correcting command Pm. Accordingly, the basic operation command  402  outputted from the basic-operation commanding module  2119  is corrected based on the automatic operation correcting command  404  reflecting the correcting manipulation accumulated in the second operation and the third operation. Therefore, the operation of the operating part  103  is corrected so as to reflect the correcting manipulations in the second operation and the third operation. As a result, after that, it becomes unnecessary for the operator to operate the manipulating part  101  for improvement. 
     As described above, according to the hydraulic excavator  20  of this embodiment, since the manual operation correcting command  403  by the operator is accumulated in the learning module  118 , the skill (here, the digging work) of the operator is consequently handed over to the learning module  118 . Moreover, since the learning module  118  learns through the actual practice, the learning period can be shorter. 
     *Case with Correcting Manipulation in Second Mode* 
     Next, a case where the operator performs correcting manipulation in Second Mode is described as one example. 
       FIGS. 24( a ) to 24( c )  are cross-sectional views schematically illustrating a process in which the digging work performed by the hydraulic excavator  20  is improved through the correcting manipulation according to the geological feature of the site to be dug. In  FIG. 24( a ) , a broken line indicates a cross-sectional shape of the hole  131  when it is assumed that the operator does not perform the correcting manipulation. 
     Referring to  FIG. 24( a ) , when the geological feature of the site where the hole  131  is planned to be dug is softer (easier to be dug) than the given geological feature assumed based on the control program for the given work, if the operating part  103  of the hydraulic excavator  20  performs the basic operation, the hole  131  having a cross-sectional shape with a deeper depth than the given cross-sectional shape is formed as indicated by the broken line. However, here, the highly-skilled operator carries out the digging while performing the correcting manipulation of the hydraulic excavator  20  in the semi-automatic mode. Then, the learning module  118  executes the machine learning of this correcting manipulation. Therefore, when the geological feature of the site where the hole  131  is to be dug after this is similar to this operation, the hydraulic excavator  20  digs the hole having the given cross-sectional shape by being controlled by the control part  401  without the correcting manipulation by the operator. 
     However, for example, when the geological feature of the site where the hole  131  is planned to be dug is harder (more difficult to be dug) than the given geological feature assumed based on the control program for the given work, the estimation basic data (e.g., the inclination, the vibration, etc., at the driver&#39;s seat when the bucket  18  is stuck into the ground, and the shape of the hole  131  imaged by the first imaging device  311  during the digging) acquired during the work is different from the data acquired when the geological feature of the site where the hole  131  is to be dug is softer than the given geological feature. Therefore, for example, the learning module  118  outputs the automatic operation correcting command  404  in which an amount of correction is zero. Accordingly, when the operator does not perform the correcting manipulation, the operating part  103  performs the basic operation based on the basic operation command, and thus, as illustrated in  FIG. 24( b ) , the hole  131  having a cross-sectional shape with a shallower depth than the given cross-sectional shape is formed, for example. 
     However, actually, the highly-skilled operator carries out the digging while performing the correcting manipulation to the operation of the operating part  103  in order to appropriately deal with this hard geological feature. As a result, as illustrated in  FIG. 24( c ) , the hole  131  having the given cross-sectional shape is formed. Then, the learning module  118  executes the machine learning of this correcting manipulation. Therefore, when the geological feature of the site where the hole  131  is to be dug after this is similar in the hardness to this operation, the hydraulic excavator  20  digs the hole having the given cross-sectional shape by being controlled by the control part  401  without the correcting manipulation by the operator. 
     As described above, various modes exist, in which the basic operation of the operating part  103  needs to be corrected. However, by using the learning module  118  which executes the machine learning like the hydraulic excavator  20 , the learning module  118  learns the manual operation correcting command  403  (accurately, the operation correcting command Pm) according to the mode every time the correction of the basic operation of the operating part  103  is required, and thus, the taking over of the skill of the operator can be achieved easily. 
     Moreover, according to the hydraulic excavator  20 , since a part of the basic operation of the operating part  103  related to the given work, which is unnecessary to be corrected, is automatically executed by the basic-operation commanding module  2119 , the operator only performs the necessary correction. Therefore, a load for the operator is reduced. Moreover, since the work varies even by the highly-skilled operator, the accuracy of the work improves when only a part of the work is performed through the manipulation by the operator as described above, compared to the case where the entire work is performed through the manipulation by the operator. 
     Conclusion 
     As described above, according to Embodiment 4, the construction machinery  200  can be provided, which is capable of learning the work performed by the construction machinery  200  while the operator performs the correcting manipulation of the basic operation of the working part  104  caused by the basic-operation commanding module  2119 , and automatically performing the learned work. 
     Accordingly, the skill-inheriting construction machinery  200  can be provided, which is capable of taking over the skill of the highly-skilled operator in the construction industry, and achieving the automation of the given work in the short period of time. 
     Moreover, according to Embodiment 4, a conventional hydraulic excavator can be remodeled to the skill-inheriting hydraulic excavator  20  of the present disclosure by adding the arithmetic unit constituting the control part  401  and the detector which acquires the estimation basic data, to the conventional hydraulic excavator. 
     Moreover, the present disclosure can be easily applied to construction machinery other than the hydraulic excavator  20 , by referring to the description of Embodiment 4. 
     Embodiment 5 
     Embodiment 5 of the present disclosure is the hydraulic excavator  20  of Embodiment 4, in which the operation-state detecting part  113  is further provided with the second imaging device  331  and the image processing module  333 . 
       FIG. 25  is a side view illustrating a configuration of hardware of the skill-inheriting hydraulic excavator  20  according to Embodiment 5.  FIG. 26  is a functional block diagram illustrating a configuration of a control system of the skill-inheriting hydraulic excavator  20  illustrated in  FIG. 25 . 
     Referring to  FIGS. 25 and 26 , in the hydraulic excavator  20  of this embodiment, the operation-state detecting part  113  is further provided with the second imaging device  331  and the image processing module  333 . The other configurations are the same as those of the hydraulic excavator  20  of Embodiment 4. 
     The second imaging device  331  entirely images the hydraulic excavator  20 . The image captured by the second imaging device  331  is applied with image processing by the processing part  333  so as to obtain data indicative of the posture of the hydraulic excavator  20 , and is outputted from the image processing module  333  as the operation-state data  213 . The optical axis  332  of the second imaging device  331  is oriented toward the hydraulic excavator  20 . 
     The second imaging device  331  is comprised of, for example, an ordinal digital camera. The second imaging device  331  is, for example, fixed to a fixed object (e.g., the ground) separated from the vehicle of the hydraulic excavator  20  via a suitable supporting member, or mounted on a drone. 
     For example, the image processing module  333  applies the image processing to the image captured by the second imaging device  331  to extract a contour of the hydraulic excavator  20 , and outputs this contour data as the operation-state data  213 . The contour data is postural data which can identify the posture of the operating part  103  of the hydraulic excavator  20 . 
     According to Embodiment 5, during the series of operation described in Embodiment 4, the posture of the hydraulic excavator  20  is identified based on the contour of the hydraulic excavator  20  in the operation-state data  213 . Therefore, the efficiency of the machine learning by the learning module  118  improves, and the automatic operation correcting command  404  outputted from the learning module  118  during the operation of the operating part  103  becomes more appropriate. 
     Embodiment 6 
     Embodiment 6 of the present disclosure exemplarily describes the hydraulic excavator  20  of Embodiment 4, in which the operation-state detecting part  113  is further provided with the sensor part  341 . 
       FIG. 27  is a side view illustrating a configuration of hardware of the skill-inheriting hydraulic excavator  20  according to Embodiment 6.  FIG. 28  is a functional block diagram illustrating a configuration of a control system of the skill-inheriting hydraulic excavator  20  illustrated in  FIG. 27 . 
     Referring to  FIG. 28 , in the hydraulic excavator  20  of this embodiment, the operation-state detecting part  113  is further provided with the sensor part  341 . The other configurations are the same as those of the hydraulic excavator  20  of Embodiment 4. The sensor part  341  is comprised of the sensors S 1  to S 4 . 
     Referring to  FIG. 27 , the hydraulic excavator  20  is provided with the sensors S 1  to S 4 . In detail, the swiveling body  15  is provided with the sensor S 1  which detects the rotational angle of the swiveling body  15  about the rotary axis A 1 . The boom  16  is provided at its base-end part with the sensor S 2  which detects the rotational angle of the boom  16  about the rotary axis A 2 . The arm  17  is provided at its base-end part with the sensor S 3  which detects the rotational angle of the arm  17  about the rotary axis A 3 . The arm  17  is provided at its tip-end part with the sensor S 4  which detects the rotational angle of the bucket  18  about the rotary axis A 4 . 
     The sensor part  341  outputs the data of the rotational angles detected by the sensors S 1  to S 4  as the operation-state data  213 . A combination of the data of the rotational angles detected by the sensors S 1  to S 4 , is the postural data which can identify the posture of the operating part  103  of the hydraulic excavator  20 . 
     According to Embodiment 6, during the series of operation described in Embodiment 4, the posture of the hydraulic excavator  20  is identified based on the combination of the rotational angles detected by the sensors S 1  to S 4  in the operation-state data  213 . Therefore, the efficiency of the machine learning by the learning module  118  improves, and the automatic operation correcting command  404  outputted from the learning module  118  during the operation of the operating part  103  becomes more appropriate. 
     Embodiment 7 
     In this embodiment of the present disclosure, a mode is exemplary described, in which the hydraulic excavator  20  of any one of Embodiments 4 to 6 is configured so that the manipulation device generates a pilot pressure, and the control valve is controlled based on the pilot pressure. 
       FIG. 29  is a functional block diagram illustrating a configuration of an operation command generating module of a skill-inheriting hydraulic excavator, which is one example of skill-inheriting construction machinery according to Embodiment 7 of the present disclosure. 
     Referring to  FIGS. 14 and 29 , in Embodiment 7, the arm manipulation device  51 , the swivel manipulation device  54 , the bucket manipulation device  57 , and the boom manipulation device  71  of the manipulating part  101  output the pairs of individual manual operation correcting commands ( 52 ,  53 ), ( 55 ,  56 ), ( 58 ,  59 ), and ( 72 ,  73 ) in pilot pressures, respectively. Moreover, the arm control valve  40 , the swivel control valve  43 , the bucket control valve  44 , and the boom control valve  47  are comprised of control valves which are controlled based on the pilot pressures. 
     Meanwhile, the operation command generating module  2120  is comprised of an adder  2120   a , a pressure/electric converter  2120   b , and an electric/pressure converter  2120   c.    
     The pressure/electric converter  2120   b  is comprised of, for example, four pairs of piezoelectric elements. The pressure/electric converter  2120   b  converts the four pairs of individual manual operation correcting commands ( 52 ,  53 ), ( 55 ,  56 ), ( 58 ), and ( 72 ,  73 ) which are pilot pressure signals outputted from the arm manipulation device  51 , the swivel manipulation device  54 , the bucket manipulation device  57 , and the boom manipulation device  71  of the manipulating part  101 , into the four pairs of individual manual operation correcting commands ( 52 ,  53 ), ( 55 ,  56 ), ( 58 ,  59 ), and ( 72 ,  73 ) which are electrical signals, respectively, and outputs them to the adder  2120   a.    
     The adder  2120   a  operates similarly to the operation command generating module  2120  of Embodiment 4. That is, the adder  2120   a  adds the four pairs of individual basic operation commands and the four pairs of individual automatic operation correcting commands to the four pairs of individual manual operation correcting commands ( 52 ,  53 ), ( 55 ,  56 ), ( 58 ,  59 ), and ( 72 ,  73 ), and generates the four pairs of individual operation commands. Then, the adder  2120   a  outputs the four pairs of individual operation commands to the electric/pressure converter  2120   c.    
     The electric/pressure converter  20   c  is comprised of multi-control valves including four pairs of electromagnetic proportional valves, and convers the four pairs of individual operation commands into the four pairs of individual operation commands ( 52 ′,  53 ′), ( 55 ′,  56 ′), ( 58 ′,  59 ′), and ( 72 ′,  73 ′) which are pilot pressure signals, and outputs them to the arm control valve  40 , the swivel control valve  43 , the bucket control valve  44 , and the boom control valve  47 , respectively. 
     Note that the four pairs of individual manual operation correcting commands ( 52 ,  53 ), ( 55 ,  56 ), ( 58 ), and ( 72 ,  73 ) constitute a part of the manual operation correcting command  403 , and the four pairs of individual operation commands ( 52 ′,  53 ′), ( 55 ′,  56 ′), ( 58 ′,  59 ′), ( 72 ′,  73 ′) constitute a part of the operation command  201 . 
     According to Embodiment 7 described above, the present disclosure can be applied to the hydraulic excavator  20  in which the manipulation device generates the manual operation correcting command in a pilot pressure, and the control valve is controlled based on the operation command in a pilot pressure. 
     Moreover, according to Embodiment 7, the conventional hydraulic excavator can be remodeled to the skill-inheriting hydraulic excavator  20  of the present disclosure by adding to the conventional hydraulic excavator the arithmetic unit constituting the control part  401 , the detector which acquires the estimation basic data, the piezoelectric elements constituting the pressure/electric converter  2120   b , and the multi-control valves constituting the electric/pressure converter  2120   c.    
     Other Embodiments 
     In the hydraulic excavator  10  of Embodiment 2, the operation-state detecting part  113  may further include the sensor part  341  of Embodiment 3. 
     Moreover, in Embodiments 1 to 3, the operation-state detecting part  113  may include, instead of or in addition to the microphone  313 , operation sensors which detect the operations of the control valves  40 ,  43 ,  44 , and  47 . The operation sensor is, for example, a pressure sensor provided to an oil channel connected to each of the control valves  40 ,  43 ,  44 , and  47 , or a position sensor which detects a position of a valve body of each of the control valves  40 ,  43 ,  44 , and  47 . 
     Moreover, in Embodiments 1 to 3, the operation-state detecting part  113  may include, instead of or in addition to the microphone  313 , a speed sensor which detects a speed of the engine  26  and outputs the detected speed data (driving-source state data). The speed sensor is, for example, a rotary encoder, a revolution indicator, or a tachometer. 
     Moreover, in Embodiments 1 to 3, the learning mode by the learning module  118  may be unsupervised learning. 
     Moreover, in Embodiments 1 to 3, the driving source of the hydraulic excavator  10  may be other than the engine  26 . Such a driving source is, for example, an electrical motor. 
     In the hydraulic excavator  20  of Embodiment 5, the operation-state detecting part  113  may further include the sensor part  341  of Embodiment 6. Moreover, this hydraulic excavator  20  may be modified as described in Embodiment 7. 
     Moreover, in any of Embodiments 4 to 7, the operation-state detecting part  113  may include, instead of or in addition to the microphone  313 , the operation sensors which detect the operations of the control valves  40 ,  43 ,  44 , and  47 . 
     Moreover, in any of Embodiments 4 to 7, the operation-state detecting part  113  may include, instead of or in addition to the microphone  313 , the speed sensor which detects the speed of the engine  26  and outputs the detected speed data (driving-source state data). The speed sensor is, for example, a rotary encoder, a revolution indicator, or a tachometer. 
     Moreover, in any of Embodiments 4 to 7, the learning mode by the learning module  118  may be unsupervised learning. 
     Moreover, in any of Embodiments 4 to 7, the driving source of the hydraulic excavator  20  may be other than the engine  26 . Such a driving source is, for example, an electrical motor. 
     In any of Embodiments 4 to 6, the operation command generating module  2120  may be configured to add the hydraulic pilot pressures with each other. 
     It is apparent for a person skilled in the art from the above description that many improvements and other embodiments of the present disclosure are possible. Therefore, the above description is to be interpreted only as illustration. 
     INDUSTRIAL APPLICABILITY 
     The construction machinery with learning function of the present disclosure is useful as construction machinery, capable of learning work performed by the construction machinery through manipulation by a human, and automatically performing the learned work. 
     Moreover, the skill-inheriting construction machinery of the present disclosure is useful as skill-inheriting construction machinery, capable of taking over skill of a highly-skilled operator in the construction industry, and achieving automation of a given work within a short period of time. 
     DESCRIPTION OF REFERENCE CHARACTERS 
     
         
           1  Hydraulic Drive System 
           8  Flow-amount Controlling Device 
           10  Hydraulic Excavator with Learning Function (Hydraulic Excavator) 
           11  Boom Cylinder 
           12  Arm Cylinder 
           13  Bucket Cylinder 
           14  Swiveling Motor 
           15  Swiveling Body 
           16  Boom 
           17  Arm 
           18  Bucket 
           19  Traveling Body 
           20  Hydraulic Excavator 
           21  First Main Pump 
           22  First Flow-amount Adjusting Device 
           23  Second Main Pump 
           24  Second Flow-amount Adjusting Device 
           25  Sub Pump 
           40  Arm Control Valve 
           43  Swivel Control Valve 
           44  Bucket Control Valve 
           47  Boom Control Valve 
           50  Acceleration Device 
           51  Arm Manipulation Device 
           54  Swivel Manipulation Device 
           57  Bucket Manipulation Device 
           71  Boom Manipulation Device 
           81 - 86 ,  91 ,  92  Pressure Sensor 
           100  Construction Machinery with Learning Function (Construction Machinery) 
           101  Manipulating Part 
           102  Body Part 
           103  Operating Part 
           104  Working Part 
           105  Hydraulic Drive System 
           106  Hydraulic Circuit 
           107  Pump Part 
           110  Operation Command Detecting Part 
           112  Work-state Detecting Part 
           113  Operation-state Detecting Part 
           114  Reaction Detecting Part 
           115  Learning Data Memory 
           116  Command Data Memory 
           117  Estimation-basic-data Memory 
           118  Learning Module 
           119  Manipulating-part Drive Part 
           120  Estimated-operation-command Converting Part 
           131  Hole 
           200  Skill-inheriting Construction Machinery 
           201  Operation Command 
           202  Drive Force 
           211 ,  211 ′ Command Data 
           212  Work-state Data 
           213  Operation-state Data 
           214  Reaction Data 
           311  First Imaging Device 
           312  Image Processing Module 
           313  Microphone 
           314  Gyroscope 
           321  Optical Axis 
           331  Second Imaging Device 
           332  Optical Axis 
           333  Image Processing Module 
           341  Sensor Part 
           400  Neural Network 
           401  Learning Data and Teacher Data Generating Module 
           402  Data Inputting Module 
           403  Learning Evaluation Module 
           500  Neural Network 
           501  Learning Data and Teacher Data Generating Module 
           502  Data Inputting Module 
           503  Learning Evaluation Module 
           1000  Construction Machinery with Learning Function 
           1101  Command Data Generating Module 
           1102  Command Memory 
           1103  Estimated Command 
           2110  Operation Command Detecting Part 
           2116  Operation-correcting-command Memory 
           2119  Basic-operation Commanding Module 
           2120  Operation Command Generating Module 
         A 1 -A 4  First to Fourth Rotary Axes 
         M 1 -M 5  Servomotor 
         Pd Estimation Basic Data 
         Pd′ Estimation Basic Data for Learning 
         Pf Estimated Operation Command 
         Pf Command Data for Learning 
         S 1 -S 4  Sensor