Patent Publication Number: US-2021178598-A1

Title: Remote control system and remote control method

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     This application is based upon and claims the benefit of priority from Japanese patent application No. 2019-225287, filed on Dec. 13, 2019, the disclosure of which is incorporated herein in its entirety by reference. 
     BACKGROUND 
     The present disclosure relates to a remote control system and a remote control method. 
     A technique is known in which a user remotely operates a device to be operated including an end effector, such as a robot or the like including a grasping part (e.g., a hand or a suction part) at the tip of its arm as an end effector, to thereby cause the device to be operated to perform a grasping motion or the like. For example, Japanese Patent No. 5326794 discloses a technique for displaying a shot image obtained by shooting the periphery of a robot and then estimating a content of an operation to be performed by the robot based on an instruction input to the shot image by a user by handwriting. 
     SUMMARY 
     However, the technique disclosed in Japanese Patent No. 5326794 is a technique for remotely controlling a robot by inputting predetermined instruction figures (o, x, A, etc.) by handwriting. Therefore, it has recently been desired to remotely control a device to be operated by a more intuitive operation. 
     The present disclosure has been made to solve the above-described problem, and it provides a remote control system and a remote control method that enable a more intuitive operation. 
     A first exemplary aspect is a remote control system configured to remotely control a device to be operated including an end effector, the remote control system including: 
     an imaging unit configured to shoot an environment in which the device to be operated is located; 
     a recognition unit configured to recognize objects that can be grasped by the end effector based on a shot image of the environment shot by the imaging unit; 
     an operation terminal configured to display the shot image and receive handwritten input information input to the displayed shot image; and 
     an estimation unit configured to, based on the objects that can be grasped which the recognition unit has recognized and the handwritten input information input to the shot image, estimate an object to be grasped which has been requested to be grasped by the end effector from among the objects that can be grasped and estimate a way of performing a grasping motion by the end effector, the grasping motion having been requested to be performed with regard to the object to be grasped. 
     Another exemplary aspect is a remote control method performed by a remote control system configured to remotely control a device to be operated including an end effector, the remote control method including: 
     shooting an environment in which the device to be operated is located; 
     receiving, by an operation terminal displaying a shot image of the environment, handwritten input information input to the displayed shot image; 
     recognizing objects that can be grasped by the end effector based on the shot image; and 
     based on the objects that can be grasped and the handwritten input information input to the shot image, estimating an object to be grasped which has been requested to be grasped by the end effector from among the objects that can be grasped and estimating a way of performing a grasping motion by the end effector, the grasping motion having been requested to be performed with regard to the object to be grasped. 
     According to the above-described aspects of the present disclosure, it is possible to provide a remote control system and a remote control method that enable a more intuitive operation. 
     The above and other objects, features and advantages of the present disclosure will become more fully understood from the detailed description given hereinbelow and the accompanying drawings which are given by way of illustration only, and thus are not to be considered as limiting the present disclosure. 
    
    
     
       BRIEF DESCRIPTION OF DRAWINGS 
         FIG. 1  is a conceptual diagram showing an example of an overall environment in which a remote control system according to an embodiment is used; 
         FIG. 2  shows an example of a first environment in which a robot is located; 
         FIG. 3  shows an example of handwritten input information; 
         FIG. 4  shows an example of handwritten input information; 
         FIG. 5  shows an example of handwritten input information; 
         FIG. 6  is an external perspective view showing an example of an external configuration of a robot; 
         FIG. 7  is a block diagram showing an example of a block configuration of the robot; 
         FIG. 8  shows an example of a shot image acquired by the robot; 
         FIG. 9  shows an example of an area that can be grasped which a first learned model outputs; 
         FIG. 10  shows an example of handwritten input information; 
         FIG. 11  shows an example of teaching data for a second learned model; 
         FIG. 12  shows an example of teaching data for the second learned model; 
         FIG. 13  is a block diagram showing an example of a block configuration of a remote terminal; 
         FIG. 14  is a flowchart showing an example of an overall flow of processes performed by the remote control system according to the embodiment; 
         FIG. 15  is a flowchart showing an example of a detailed flow of processes performed in Step S 13  of  FIG. 14 ; 
         FIG. 16  shows an example of handwritten input information; and 
         FIG. 17  is a diagram showing an example in which a plurality of handwritten input information pieces are input. 
     
    
    
     DESCRIPTION OF EMBODIMENTS 
     Hereinafter, although the present disclosure will be described with reference to embodiment of the present disclosure, the present disclosure according to claims is not limited to the following embodiment. Further, all the components described in the following embodiment are not necessarily essential as means for solving problems. Further, in the following embodiment, a robot including a hand at the tip of its arm as an end effector will be described as an example of a device to be operated, but the device to be operated is not limited thereto. 
       FIG. 1  is a conceptual diagram showing an example of an overall environment in which a remote control system  10  according to this embodiment is used. A robot  100  that performs various kinds of motions in a first environment is remotely controlled via a system server  500  connected to an Internet  600  by allowing a user who is a remote operator present in a second environment distant from the first environment to operate a remote terminal  300  (an operation terminal). In the first environment, the robot  100  is connected to the Internet  600  via a wireless router  700 . Further, in the second environment, the remote terminal  300  is connected to the Internet  600  via the wireless router  700 . The system server  500  is connected to the Internet  600 . The robot  100  performs a grasping motion or the like by a hand  124  in accordance with an operation of the remote terminal  300  by the user. 
     Note that in this embodiment, grasping motions performed by the hand  124  are not limited to motions for simply grasping (holding) an object to be grasped, but also include, for example, the following motions.
         a motion for holding and lifting an object to be grasped   a motion for, when an object to be grasped is a knob for a door or a drawer of a cabinet or the like, holding the knob and then opening and closing the door or the drawer   a motion for, when an object to be grasped is a door knob of a door, holding the door knob and then opening and closing the door       

     The robot  100  shoots the first environment in which the robot  100  is located by a stereo camera  131  (an imaging unit), and transmits the shot image to the remote terminal  300  via the Internet  600 . Further, the robot  100  recognizes objects that can be grasped by the hand  124  based on the shot image. 
       FIG. 2  shows an example of a first environment in which the robot  100  is located. In the example shown in  FIG. 2 , a table  400 , cabinets  410 ,  420 , and  430 , and a door  440  are located in the first environment. The objects that can be grasped located in the first environment are objects  401  and  402  placed on the table  400 , a knob  411  of the cabinet  410 , knobs  421  and  422  of the cabinet  420 , knobs  431  and  432  of the cabinet  430 , and a door knob  441  of the door  440 . 
     The remote terminal  300  is, for example, a tablet terminal, and includes a display panel  341  on which a touch panel is superimposed. The shot image received from the robot  100  is displayed on the display panel  341 , and thus a user can indirectly view the first environment in which the robot  100  is located. Further, a user can input handwritten input information, which is an image (a first image) that simulates a way of grasping the object to be grasped which has been requested to be grasped by the hand  124 , to the shot image displayed on the display panel  341  by handwriting. As a method for inputting the handwritten input information, for example, a method in which the relevant part of the shot image on a touch panel disposed so as to be superimposed on the display panel  341  is touched with a finger of a user, a touch pen, or the like can be used, but the method therefor is not limited to this. Each of  FIGS. 3 to 5  shows an example of handwritten input information input to a shot image  310 . The example shown in  FIG. 3  shows handwritten input information  901  that simulates grasping the object  401  having a rectangular parallelepiped shape placed on the table  400  from above. The example of  FIG. 4  shows handwritten input information  902  that simulates grasping the object  402  having a columnar shape placed on the table  400  from the side. The example of  FIG. 5  shows handwritten input information  903  that simulates grasping the knob  411  of the cabinet  410  and then opening the door. As shown in  FIGS. 3 to 5 , the image of the handwritten input information may be an image consisting of only figures such as lines, or an image consisting of a combination of figures such as lines and characters. The handwritten input information which a user has input to the shot image is transmitted to the robot  100  via the Internet  600 . 
     The robot  100 , based on objects that can be grasped which have been recognized from a shot image and handwritten input information which a user has input to the shot image, estimates an object to be grasped which has been requested to be grasped by the hand  124  from among the objects that can be grasped and estimates a way of performing a grasping motion by the hand  124 , the grasping motion having been requested to be performed with regard to the object to be grasped. 
       FIG. 6  is an external perspective view showing an example of an external configuration of the robot  100 . The robot  100  includes, mainly, a movable base part  110  and a main-body part  120 . The movable base part  110  supports two driving wheels  111  and a caster  112 , each of which is in contact with a traveling surface, inside its cylindrical housing. The two driving wheels  111  are arranged so that the centers of their rotation axes coincide with each other. Each of the driving wheels  111  is rotationally driven by a motor (not shown) independently of each other. The caster  112  is a driven wheel and is disposed so that its pivotal axis extending from the movable base part  110  in the vertical direction axially supports the wheel at a place away from its rotation axis. Further, the caster  112  follows the movement of the movable base part  110  so as to move in the moving direction of the movable base part  110 . 
     The movable base part  110  includes a laser scanner  133  in a peripheral part of its top surface. The laser scanner  133  scans a certain range on the horizontal plane at intervals of a certain stepping angle and outputs information as to whether or not there is an obstacle in each direction. Further, when there is an obstacle, the laser scanner  133  outputs a distance to the obstacle. 
     The main-body part  120  includes, mainly, a body part  121  mounted on the top surface of the movable base part  110 , a head part  122  placed on the top surface of the body part  121 , an arm  123  supported on the side surface of the body part  121 , and the hand  124  disposed at the tip of the arm  123 . The arm  123  and the hand  124  are driven by motors (not shown) and grasp an object to be grasped. The body part  121  is able to rotate around a vertical axis with respect to the movable base part  110  by a driving force of a motor (not shown). 
     The head part  122  mainly includes the stereo camera  131  and a display panel  141 . The stereo camera  131  has a configuration in which two camera units having the same angle of view are arranged away from each other, and outputs imaging signals of images shot by the respective camera units. 
     The display panel  141  is, for example, a liquid crystal display panel, and displays an animated face of a pre-defined character and displays information about the robot  100  in the form of text or by using icons. By displaying the face of the character on the display panel  141 , it is possible to impart an impression that the display panel  141  is a pseudo face part to people around the robot  100 . 
     The head part  122  is able to rotate around a vertical axis with respect to the body part  121  by a driving force of a motor (not shown). Thus, the stereo camera  131  can shoot an image in any direction. Further, the display panel  141  can show displayed contents in any direction. 
       FIG. 7  is a block diagram showing an example of a block configuration of the robot  100 . Main elements related to an estimation of an object to be grasped and an estimation of a way of performing a grasping motion will be described below. However, the robot  100  includes elements in its configuration other than the above ones and may include additional elements that contribute to the estimation of an object to be grasped and the estimation of a way of performing a grasping motion. 
     A control unit  150  is, for example, a CPU (Central Processing Unit) and is included in, for example, a control box disposed in the body part  121 . A movable-base drive unit  145  includes the driving wheels  111 , and a driving circuit and motors for driving the driving wheels  111 . The control unit  150  performs rotation control of the driving wheels by sending a driving signal to the movable-base drive unit  145 . Further, the control unit  150  receives a feedback signal such as an encoder signal from the movable-base drive unit  145  and recognizes a moving direction and a moving speed of the movable base part  110 . 
     An upper-body drive unit  146  includes the arm  123  and the hand  124 , the body part  121 , the head part  122 , and driving circuits and motors for driving these components. The control unit  150  performs a grasping motion and a gesture by transmitting a driving signal to the upper-body drive unit  146 . Further, the control unit  150  receives a feedback signal such as an encoder signal from the upper-body drive unit  146 , and recognizes positions and moving speeds of the arm  123  and the hand  124 , and orientations and rotation speeds of the body part  121  and the head part  122 . 
     The display panel  141  receives an image signal generated by the control unit  150  and displays an image thereof. Further, as described above, the control unit  150  generates an image signal of the character or the like and displays an image thereof on the display panel  141 . 
     The stereo camera  131  shoots the first environment in which the robot  100  is located in accordance with a request from the control unit  150  and passes an obtained imaging signal to the control unit  150 . The control unit  150  performs image processing by using the imaging signal and converts the imaging signal into a shot image in a predetermined format. The laser scanner  133  detects whether there is an obstacle in the moving direction of the robot  100  in accordance with a request from the control unit  150  and passes a detection signal, which is a result of the detection, to the control unit  150 . 
     A hand camera  135  is, for example, a distance image sensor, and is used to recognize a distance to an object to be grasped, a shape of an object to be grasped, a direction of an object to be grasped, and the like. The hand camera  135  includes an image pickup device in which pixels for performing a photoelectrical conversion of an optical image incident from a target space are two-dimensionally arranged, and outputs a distance to the subject to the control unit  150  for each of the pixels. Specifically, the hand camera  135  includes an irradiation unit that irradiates a pattern light to the target space, and receives the reflected light of the pattern light by the image pickup device to output a distance to the subject captured by each of the pixels based on a distortion and a size of the pattern in the image. Note that the control unit  150  recognizes a state of a wider surrounding environment by the stereo camera  131  and recognizes a state in the vicinity of the object to be grasped by the hand camera  135 . 
     A memory  180  is a nonvolatile storage medium. For example, a solid-state drive is used for the memory  180 . The memory  180  stores, in addition to a control program for controlling the robot  100 , various parameter values, functions, lookup tables, and the like used for the control and the calculation. In particular, the memory  180  stores a first learned model  181  that uses a shot image as an input image and outputs objects that can be grasped shown in the shot image, and a second learned model  182  that uses handwritten input information as an input image and outputs the meaning of a grasping motion simulated by the handwritten input information. 
     A communication unit  190  is, for example, a wireless LAN unit and performs radio communication with the wireless router  700 . The communication unit  190  receives the handwritten input information sent from the remote terminal  300  and passes it to the control unit  150 . Further, the communication unit  190  transmits a shot image shot by the stereo camera  131  to the remote terminal  300  under the control of the control unit  150 . 
     The control unit  150  performs control of the whole robot  100  and various calculation processes by executing a control program read from the memory  180 . Further, the control unit  150  also serves as a function execution unit that executes various calculations and controls related to the control. As such function execution units, the control unit  150  includes a recognition unit  151  and an estimation unit  152 . 
     The recognition unit  151  uses a shot image shot by one of the camera units of the stereo camera  131  as an input image, obtains areas that can be grasped by the hand  124  shown in the shot image from the first learned model  181  read from the memory  180 , and recognizes objects that can be grasped. 
       FIG. 8  is a diagram showing an example of the shot image  310  of the first environment which the robot  100  has acquired by the stereo camera  131 . The shot image  310  in  FIG. 8  shows the cabinet  410  having the knob  411  and the cabinet  420  having the knobs  421  and  422 . The recognition unit  151  provides the shot image  310  described above to the first learned model  181  as an input image. 
       FIG. 9  is a diagram showing an example of the areas that can be grasped output by the first learned model  181  when the shot image  310  shown in  FIG. 8  is used as an input image. Specifically, an area that surrounds the knob  411  is detected as an area  801  that can be grasped, an area that surrounds the knob  421  is detected as an area  802  that can be grasped, and an area that surrounds knob  422  is detected as an area  803  that can be grasped. Thus, the recognition unit  151  recognizes each of the knobs  411 ,  421 , and  422 , which are surrounded by the respective areas  801 ,  802 , and  803  that can be grasped, as an object that can be grasped. 
     The first learned model  181  is a neural network learned from teaching data which is a combination of an image showing objects that can be grasped by the hand  124  and a correct answer to which area of the image is the object that can be grasped. At this time, by preparing the teaching data so that it further indicates the distances to the objects that can be grasped and the directions in which the objects that can be grasped are located shown in the image, the first learned model  181 , which uses the shot image as an input image, can output not only the objects that can be grasped but also the distances to the objects that can be grasped and the directions in which the objects that can be grasped are located. Note that the first learned model  181  may be a neural network learned by deep learning. Further, teaching data may be added to the first learned model  181  as necessary so that it performs additional learning. 
     The estimation unit  152 , based on objects that can be grasped which the recognition unit  151  has recognized from a shot image and handwritten input information which a user has input to the shot image, estimates an object to be grasped which has been requested to be grasped by the hand  124  from among the objects that can be grasped which the recognition unit  151  has recognized and estimate a way of performing a grasping motion by the hand  124 , the grasping motion having been requested to be performed with regard to the estimated object to be grasped. 
       FIG. 10  is a diagram showing an example of handwritten input information which a user has input to the shot image  310  of  FIG. 8  in the remote terminal  300 . In the example shown in  FIG. 10 , handwritten input information  904  is input to a position on the knob  411  on the shot image  310 . Therefore, the estimation unit  152  estimates that the object to be grasped which has been requested to be grasped by the hand  124  is the knob  411  among the knobs  411 ,  421 , and  422  which the recognition unit  151  has recognized as the objects that can be grasped. Note that the estimation unit  152  can recognize the input position of the handwritten input information  904  on the shot image  310  by any method. For example, if the remote terminal  300  includes, in the handwritten input information  904 , position information indicating the input position of the handwritten input information  904  on the shot image  310  and transmit this handwritten input information  904 , the estimation unit  152  can recognize the input position of the handwritten input information  904  based on this position information. 
     Alternatively, if the remote terminal  300  transmits the shot image  310  processed into a state in which the handwritten input information  904  has been input, the estimation unit  152  can recognize the input position of the handwritten input information  904  based on this shot image  310 . 
     Further, the estimation unit  152  uses an image of handwritten input information which a user has input to the shot image as an input image, obtains the meaning of a grasping motion simulated by this handwritten input information from the second learned model  182  read from the memory  180 , and estimates a way of performing a grasping motion by the hand  124 , the grasping motion having been requested to be performed with regard to the object to be grasped. 
     The second learned model  182  is a neural network learned from teaching data which is a combination of an image of handwritten input information and the meaning of a grasping motion simulated by this handwritten input information.  FIG. 11  shows an example of teaching data for the second learned model  182 . The example of the teaching data shown in  FIG. 11  is teaching data for making the second learned model  182  learn three images indicating a grasping motion of “holding something” and four images indicating a grasping motion of “opening something”. Further, the second learned model  182  may be made to learn more detailed teaching data than the teaching data shown in  FIG. 11 .  FIG. 12  is a diagram showing an example of teaching data for making the second learned model  182  learn in more detail a grasping motion of “holding something”. The example of the teaching data shown in  FIG. 12  is teaching data for making the second learned model  182  learn an image indicating a grasping motion of “holding something from above”, an image indicating a grasping motion of “holding something from the side”, and an image indicating a grasping motion of “holding something from diagonally above”. Note that the second learned model  182  may be a neural network learned by deep learning. Further, teaching data may be added to the second learned model  182  as necessary so that it performs additional learning. 
     In the example shown in  FIG. 10 , the estimation unit  152  recognizes from the second learned model  182  that the handwritten input information  904  means a grasping motion of “opening something”. Therefore, the estimation unit  152  estimates that the grasping motion which has been requested to be performed with regard to the knob  411 , which is the object to be grasped, is a motion of holding the knob  411  and then opening the door. 
     As described above, the estimation unit  152  can estimate an object to be grasped which has been requested to be grasped by the hand  124  and a way of performing a grasping motion by the hand  124 , the grasping motion having been requested to be performed with regard to the object to be grasped. Further, depending on the first learned model  181 , it is possible to obtain the distance to the object to be grasped and the direction in which the object to be grasped is located from the shot image acquired by the stereo camera  131 . Note that the distance to the object to be grasped and the direction in which the object to be grasped is located may be obtained by performing an image analysis on the shot image of the first environment or from information received from other sensors. Further, it is also possible to detect whether there are obstacles in the moving direction of the robot  100  by a detection signal received from the laser scanner  133 . 
     Therefore, the control unit  150  generates, based on the distance to the object to be grasped, the direction in which the object to be grasped is located, the presence or absence of obstacles, and the like, a route for the robot  100  to move from its current position to the vicinity of the object to be grasped while avoiding obstacles, and transmits a driving signal corresponding to the generated route to the movable-base drive unit  145 . The movable-base drive unit  145  moves the robot  100  to the vicinity of the object to be grasped in response to the driving signal. 
     When the robot  100  has been moved to the vicinity of the object to be grasped, the control unit  150  makes preparations to start performing a grasping motion by the hand  124 , the grasping motion having been requested to be performed with regard to the object to be grasped. Specifically, first, the control unit  150  drives the arm  123  to a position where the hand camera  135  can observe an object to be grasped. Next, the control unit  150  causes the hand camera  135  to shoot the object to be grasped and thus recognizes the state of the object to be grasped. 
     Then, the control unit  150  generates a trajectory of the hand  124  for enabling a grasping motion that has been requested to be performed with regard to the object to be grasped based on the state of the object to be grasped and a way of performing the grasping motion by the hand  124 . At this time, the control unit  150  generates a trajectory of the hand  124  so that it satisfies predetermined grasping conditions. The predetermined grasping conditions include the condition at the time when the hand  124  grasps the object to be grasped, condition of the trajectory of the hand  124  until the hand  124  grasps the object to be grasped, and the like. Examples of the conditions at the time when the hand  124  grasps the object to be grasped include that the arm  123  is prevented from extending too much when the hand  124  grasps the object to be grasped. Further, examples of the conditions of the trajectory of the hand  124  until the hand  124  grasps the object to be grasped include that the hand  124  describes a straight trajectory when the object to be grasped is a knob for a drawer. 
     When the control unit  150  generates a trajectory of the hand  124 , it transmits a driving signal corresponding to the generated trajectory to the upper-body drive unit  146 . The hand  124  performs a grasping motion with regard to the object to be grasped in response to the driving signal. 
       FIG. 13  is a block diagram showing an example of a block configuration of the remote terminal  300 . Main elements related to a process for inputting handwritten input information to a shot image received from the robot  100  will be described below. However, the remote terminal  300  includes elements in its configuration other than the above ones and may include additional elements that contribute to the process for inputting handwritten input information. 
     A calculation unit  350  is, for example, a CPU and performs control of the whole remote terminal  300  and various calculation processes by executing a control program read from a memory  380 . The display panel  341  is, for example, a liquid crystal panel, and displays, for example, a shot image sent from the robot  100 . 
     An input unit  342  includes a touch panel disposed so as to be superimposed on the display panel  141  and a push button provided on a peripheral part of the display panel  141 . The input unit  342  passes, to the calculation unit  350 , the handwritten input information, which is an image that simulates a way of performing a grasping motion by hand  124 , the grasping motion having been requested to be performed with regard to the object to be grasped, and which a user has input by touching the touch panel. Examples of the handwritten input information are as shown in  FIGS. 3 to 5 . 
     The memory  380  is a nonvolatile storage medium. For example, a solid-state drive is used for the memory  380 . The memory  380  stores, in addition to a control program for controlling the remote terminal  300 , various parameter values, functions, lookup tables, and the like used for the control and the calculation. 
     A communication unit  390  is, for example, a wireless LAN unit and performs radio communication with the wireless router  700 . The communication unit  390  receives the shot image sent from the robot  100  and passes it to the calculation unit  350 . Further, the communication unit  390  cooperates with the calculation unit  350  to transmit handwritten input information to the robot  100 . 
     Next, an overall description is given of processes performed by the remote control system  10  according to this embodiment.  FIG. 14  is a flowchart showing an example of an overall flow of the processes performed by the remote control system  10  according to this embodiment. The flow on the left side thereof represents a flow of processes performed by the robot  100 , and the flow on the right side thereof represents a flow of processes performed by the remote terminal  300 . Further, exchanges of handwritten input information and a shot image via the system server  500  are indicated by dotted-line arrows. 
     The control unit  150  of the robot  100  causes the stereo camera  131  to shoot the first environment in which the robot  100  is located (Step S 11 ), and transmits the shot image to the remote terminal  300  via the communication unit  190  (Step S 12 ). 
     When the calculation unit  350  of the remote terminal  300  receives the shot image from the robot  100  via the communication unit  390 , the calculation unit  350  displays the received shot image on the display panel  341 . After that, the calculation unit  350  causes the display panel  341  to transit to a state in which handwritten input information input to the shot image can be received (Step S 31 ). When a user inputs handwriting input information to the shot image via the input unit  342  which is a touch panel (Yes in Step S 31 ), the calculation unit  350  transmits the handwriting input information to the robot  100  via the communication unit  390  (Step S 32 ). 
     When the recognition unit  151  of the robot  100  receives the handwritten input information which a user has input to the shot image from the remote terminal  300 , the recognition unit  151  recognizes objects that can be grasped based on this shot image. The estimation unit  152  of the robot  100 , based on the objects that can be grasped which the recognition unit  151  has recognized from the shot image and handwritten input information which a user has input to the shot image, estimates an object to be grasped which has been requested to be grasped by the hand  124  from among the objects that can be grasped which the recognition unit  151  has recognized and estimates a way of performing a grasping motion by the hand  124 , the grasping motion having been requested to be performed with regard to the estimated object to be grasped (Step S 13 ). 
     After that, the control unit  150  of the robot  100  controls the movable-base drive unit  145  so as to move the robot  100  to the vicinity of the object to be grasped, and when the robot  100  has been moved to the vicinity of the object to be grasped, the control unit  150  generates a trajectory of the hand  124  for enabling the grasping motion that has been requested to be performed with regard to the object to be grasped (Step S 14 ). When the control unit  150  has generated a trajectory of the hand  124 , the control unit  150  controls the upper-body drive unit  146  in accordance with the generated trajectory, whereby the grasping motion is performed by the hand  124  to the object to be grasped (Step S 15 ). 
     Next, processes performed in Step S 13  of  FIG. 14  are described in detail.  FIG. 15  is a flowchart showing an example of a detailed flow of the processes performed in Step S 13  of  FIG. 14  by the robot  100 . When handwritten input information which a user has input to a shot image is received from the remote terminal  300 , the recognition unit  151  first uses the shot image as an input image, obtains areas that can be grasped shown in the shot image from the first learned model  181  read from the memory  180 , and recognizes objects that can be grasped (Step S 131 ). 
     Next, the estimation unit  152  estimates an object to be grasped which has been requested to be grasped by the hand  124  from among the objects that can be grasped which the recognition unit  151  has recognized based on the input position of the handwritten input information on the shot image (Step S 132 ). Note that the input position of the handwritten input information on the shot image may be recognized by using, for example, a method similar to that described above. 
     Next, the estimation unit  152  uses an image of the handwritten input information as an input image, obtains the meaning of a grasping motion simulated by this handwritten input information from the second learned model  182  read from the memory  180 , and estimates a way of performing a grasping motion by the hand  124 , the grasping motion having been requested to be performed with regard to the object to be grasped (Step S 133 ). 
     As described above, according to this embodiment, the recognition unit  151  recognizes objects that can be grasped by the hand  124  based on a shot image of the environment in which the robot  100  is located. The estimation unit  152 , based on the objects that can be grasped which the recognition unit  151  has recognized from the shot image and handwritten input information which a user has input to the shot image, estimates an object to be grasped which has been requested to be grasped by the hand  124  from among the objects that can be grasped and estimate a way of performing a grasping motion by the hand  124 , the grasping motion having been requested to be performed with regard to the estimated object to be grasped. 
     By this configuration, it is possible to have the robot  100  perform a grasping motion by a remote control without the need for a user to recollect predetermined instruction figures and input them by handwriting. Thus, it is possible to implement the remote control system  10  that enables a more intuitive operation. 
     Note that the present disclosure is not limited to the above-described embodiment and can be modified as appropriate without departing from the spirit of the present disclosure. 
     For example, in the above-described embodiment, handwritten input information is an image that simulates a way of performing a grasping motion by the hand  124 , the grasping motion having been requested to be performed with regard to the object to be grasped, but this is merely an example. The handwritten input information may further include an image that shows a level of the grasping motion. In this case, the estimation unit  152  may further estimate a level of the grasping motion that has been requested to be performed with regard to the object to be grasped based on the handwritten input information.  FIG. 16  is a diagram showing an example of handwritten input information including an image that shows a level of the grasping motion. The example shown in  FIG. 16  shows handwritten input information  905  in which an image of “30°” showing the level of the grasping motion is added to an image similar to the handwritten input information  903  of  FIG. 5 . In the case of the example shown in  FIG. 16 , the estimation unit  152  estimates that a grasping motion which has been requested to be performed with regard to the knob  411  is a motion for holding the knob  411  and then opening the door by 30°. This configuration enables a user to perform a more detailed and intuitive operation. 
     Further, in the above-described embodiment, an example in which one piece of handwritten input information is input to the shot image has been described, but this is merely one example. A plurality of handwritten input information pieces may be input to the shot image. When a plurality of handwritten input information pieces are input to the shot image, the estimation unit  152  estimates an object to be grasped and a way of performing a grasping motion for the respective plurality of handwritten input information pieces. At this time, the estimation unit  152  may estimate that the order of performing the grasping motions is the order in which the handwritten input information pieces corresponding to the grasping motions are input. Alternatively, each of the handwritten input information pieces may further include an image showing the order of performing the grasping motions, and the estimation unit  152  may further estimate the order of performing the grasping motions based on these handwritten input information pieces.  FIG. 17  is a diagram showing an example in which a plurality of handwritten input information pieces, each of which includes an image showing the order of performing the grasping motions, have been input.  FIG. 17  shows an example in which two pieces of handwritten input information of handwritten input information  906  for the knob  422  and handwritten input information  907  for the knob  411  are input to the shot image  310 . Here, the handwritten input information  906  includes an image of “1” showing the order of performing the grasping motions, and the handwritten input information  907  includes an image of “2” showing the order of performing the grasping motions. Therefore, in the case of the example shown in  FIG. 17 , the estimation unit  152  estimates that the grasping motion (the motion for holding the knob  422  and then opening the drawer) which has been requested to be performed with regard to the knob  422  is performed first and the grasping motion (the motion for holding the knob  411  and then opening the door) which has been requested to be performed with regard to the knob  411  is performed second. 
     Further, in the above-described embodiment, two learned models of the first and the second learned models  181  and  182  are used, but this is merely an example. Instead of using the first and the second learned models  181  and  182 , a transfer learned model in which the output of the first learned model  181  is applied to the second learned model  182  may be used. The transfer learned model is, for example, a model that uses a shot image in which handwritten input information has been input as an input image and outputs objects that can be grasped shown in the shot image, an object to be grasped among the objects that can be grasped, and the meaning of a grasping motion that is simulated by the handwritten input information and is performed with regard to the object to be grasped. 
     Further, in the above-described embodiment, the robot  100  includes the recognition unit  151  and the estimation unit  152 , but this is merely an example. The recognition unit  151  and the estimation unit  152  may be included in the remote terminal  300  or in the system server  500 . 
     Further, in the above-described embodiment, the robot  100  and the remote terminal  300  exchange a shot image and handwritten input information via the Internet  600  and the system server  500 , but this is merely an example. The robot  100  and the remote terminal  300  may exchange a shot image and handwritten input information by direct communication. 
     Further, in the above-described embodiment, the imaging unit (the stereo camera  131 ) included in the robot  100  is used, but this is merely an example. The imaging unit may be any imaging unit provided at any place in the first environment in which the robot  100  is located. Further, the imaging unit is not limited to a stereo camera and may be a monocular camera or the like. 
     Further, in the above-described embodiment, the example in which the device to be operated is the robot  100  including the hand  124  at the tip of the arm  123  as an end effector has been described, but this is merely one example. The device to be operated may be any object including an end effector and performing a grasping motion by using the end effector. Further, the end effector may be a grasping part (e.g., a suction part) other than a hand. Further, in the above-described embodiment, it has been described that in the robot  100  and the remote terminal  300 , the CPU executes the control program read from the memory, thereby performing control and calculation processes. In the system server  500 , like in the robot  100  and the remote terminal  300 , the CPU may also execute the control program read from the memory, thereby performing control and calculation processes. 
     In the above-described examples, the program can be stored and provided to a computer using any type of non-transitory computer readable media. Non-transitory computer readable media include any type of tangible storage media. Examples of non-transitory computer readable media include magnetic storage media (such as floppy disks, magnetic tapes, hard disk drives, etc.), optical magnetic storage media (e.g., magneto-optical disks), CD-ROM (compact disc read only memory), CD-R (compact disc recordable), CD-R/W (compact disc rewritable), and semiconductor memories (such as mask ROM, PROM (programmable ROM), EPROM (erasable PROM), flash ROM, RAM (random access memory), etc.). The program may be provided to a computer using any type of transitory computer readable media. 
     Examples of transitory computer readable media include electric signals, optical signals, and electromagnetic waves. Transitory computer readable media can provide the program to a computer via a wired communication line (e.g., electric wires, and optical fibers) or a wireless communication line. 
     From the disclosure thus described, it will be obvious that the embodiments of the disclosure may be varied in many ways. Such variations are not to be regarded as a departure from the spirit and scope of the disclosure, and all such modifications as would be obvious to one skilled in the art are intended for inclusion within the scope of the following claims.