Patent Publication Number: US-6711467-B2

Title: Robot apparatus and its control method

Description:
TECHNICAL FIELD 
     The present invention relates to a robot apparatus and control method for the same, and more particularly, is suitably applied to a pet robot. 
     BACKGROUND ART 
     In recent years, a walking type pet robot with four legs which acts according to commands from a user and the surrounding environments has been proposed and developed by the assignee of this invention. Such pet robot looks like a dog or a cat which is kept in a general house and autonomously acts according to commands from a user and the surrounding environments. It should be noted that the word “behavior” is used for indicating a group of actions hereinafter. 
     If such pet robot has a function of adapting the life rhythm of the pet robot to the life rhythm of a user, the pet robot can be considered to have a further improved amusement property and as a result, the user will get a larger sense of affinity and satisfaction. 
     DESCRIPTION OF THE INVENTION 
     The present invention is made in view of the above points and intends to a robot apparatus and a control method for the same which can offer an improved amusement property. 
     The foregoing object and other objects of the invention have been achieved by the provision of a robot apparatus and a control method for the same, in which a history of user use is created in a temporal axis direction and is stored in a storage means and next behavior is determined based on the history of use. As a result, in the robot apparatus and control method for the same, life rhythm of the robot apparatus can be adapted to the life rhythm of the user, thus making it possible to realize a robot apparatus having a further improved entertainment property and a control method for the same so that a user can get a larger sense of affinity out of the robot. 
     Further, in the robot apparatus and control method for the same of the present invention, behavior of the robot apparatus is determined based on a cycle parameters which allows behavior of the robot apparatus to have a cyclic tendency for each prescribed time period, and each part of the robot apparatus is driven based on the determined behavior. As a result, in the robot apparatus and control method for the same, the life rhythm of the robot apparatus can be adapted to the life rhythm of the user, thus making it possible to realize a robot apparatus having a further improved entertainment property and a control method for the same so that a user can get a larger sense of affinity. 
     Furthermore, in the robot apparatus and control method for the same of the present invention, an external stimulus which is detected by a prescribed external stimulus detecting means is evaluated to judge whether the stimulus was from a user, the external stimulus from the user is converted into a predetermined numerical parameter and behavior is determined based on the parameter, and then each part of the robot apparatus is driven based on the determined behavior. As a result, in the robot apparatus and control method for the same, the life rhythm of the robot apparatus can be adapted to the life rhythm of the user, thus making it possible to realize a robot apparatus having a further improved entertainment property and a control method for the same so that a user can get a larger sense of affinity. 
    
    
     BRIEF DESCRIPTION OF THE DRAWINGS 
     FIG. 1 is a perspective view showing an external structure of a pet robot to which the present invention is applied; 
     FIG. 2 is a block diagram showing a circuit arrangement of the pet robot; 
     FIG. 3 is a concept diagram showing growth model; 
     FIG. 4 is a block diagram explaining controller&#39;s processing; 
     FIG. 5 is a concept diagram explaining data processing in a emotion/instinct model section; 
     FIG. 6 is a concept diagram showing probability automatons; 
     FIG. 7 is a concept diagram showing a table of state transitions. 
     FIG. 8 is a concept diagram explaining a directed graph; 
     FIG. 9 shows schematic diagrams explaining awakening parameter tables; 
     FIG. 10 is a flowchart showing a processing procedure of creating the awakening parameter table; 
     FIG. 11 is a schematic diagram explaining of obtaining an interaction level; and 
     FIG. 12 shows schematic diagrams explaining awakening parameter tables according another embodiment. 
    
    
     BEST MODE FOR CARRYING OUT THE INVENTION 
     Preferred embodiments of this invention will be described with reference to the accompanying drawings: 
     Referring to FIG. 1, reference numeral  1  shows a pet robot in which leg units  3 A to  3 D are attached to the front, rear, left, and right of a body unit  2  and a head unit  4  and a tail unit  5  is attached to the front end and the rear end of the body unit  2 . 
     In this case, the body unit  2  contains a controller  10  for controlling whole motions of the pet robot  1 , a battery  11  serving as a power source of the pet robot  1 , and an internal sensor section  15  composed of a battery sensor  12 , a thermal sensor  13  and an acceleration sensor  14  as shown in FIG.  2 . 
     The head unit  4  is provided with an external sensor section  19  composed of a microphone  16  which is for “ears” of the pet robot  1 , a CCD (Charge Coupled Device) camera  17  which is for “eyes” and a touch sensor  18 , a speaker  20  which is for “mouth” and so on, at fixed positions. 
     Further, actuators  21   1  to  21   n  are installed in the joints of the leg units  3 A to  3 D, the jointing parts of the leg units  3 A to  3 D and the body unit  2 , the jointing part of the head unit  4  and the body unit  2 , and the jointing part of the tail unit  5  and the body unit  2 . 
     The microphone  16  of the external sensor section  19  receives a command sound indicating “walk”, “lie down”, or “chase a ball” which is given from a user by scales via a sound commander not shown, and transmits the obtained audio signal S 1 A to the controller  10 . Further, the CCD camera  17  takes a photo of surrounding conditions and sends the obtained video signal S 1 B to the controller  10 . 
     Further, the touch sensor  18  is provided on the top of the head unit  4  as can be seen from FIG. 1, to detect pressure which is generated by a user&#39;s physical spur such as “stroking” or “hit” and then transmits the detection result as a pressure detection signal S 1 C to the controller  10 . 
     The battery sensor  12  of the internal sensor section  15  detects the energy level of the battery  11  and transmits the detection result as a battery level detection signal S 2 A to the controller  10 . The thermal sensor  13  detects an internal temperature of the pet robot  1  and transmits the detection result as a temperature detection signal S 2 B to the controller  10 . The acceleration sensor  14  detects accelerations in three axis directions (Z axis direction, Y axis direction and Z axis direction) and transmits the detection result as an acceleration detection signal S 2 C to the controller  10 . 
     The controller  10  judges the external and internal states, commands from a user and the existence of a spur from a user, based on the audio signal S 1 A, video signal S 1 B and pressure detection signal S 1 C (hereinafter, they are referred to as an external information signal S 1  altogether) given from the external sensor section  19 , the battery level signal S 2 A, temperature detection signal S 2 B and acceleration detection signal S 2 C (hereinafter, they are referred to as an internal information signal S 2  altogether) given from the internal sensor section  15 . 
     Then, the controller  10  determines next behavior based on the judgement result and a control program which has been stored in the memory  10 A in advance, and drives necessary actuators  21   1  to  21   n  based on the determination result, so as to make behavior or an action, for example, to move the head unit  4  up, down, right and left, to move a tail  5 A of the tail unit  5 , to move the leg units  3 A to  3 D for walking, or the like. 
     At this point, the controller  10  generates the audio signal S 3 , if necessary, and gives it to the speaker  20 , so as to output sounds based on the audio signal S 3  to outside or to blink LEDs (Light Emitting Diode), not shown, which are installed at the “eye” positions of the pet robot  1 . 
     In this way, the pet robot  1  can autonomously behave according to the external and internal states, commands from a user, spurs from a user and the like. 
     In addition to the aforementioned operation, the pet robot  1  is arranged to change its behavior and actions according to a history of operation inputs such as spurs and commands with the sound commander from a user and a history of its own behavior and actions, as if a real animal grows. 
     That is, the pet robot  1  has four “growth steps” of “babyhood”, “childhood”, “younghood” and “adulthood” as a growth process as shown in FIG.  3 . And the memory  10 A of the controller  10  stores behavior and action models made up from various control parameters and control programs, as a basis of behavior and actions relating to “walking”, “motion (motion)”, “behavior” and “sound (sound)”, for each “growth step”. 
     Therefore, the pet robot  1  “grows” based on the four steps of “babyhood”, “childhood”, “younghood”, and “adulthood”, according to the histories of inputs from outside and of its own behavior and actions. 
     Note that, as known from FIG. 3, this embodiment provides a plurality of behavior and action models for each of “growth steps” of “childhood”, “younghood” and “adulthood”. 
     Thus, the pet robot  1  can change “behavior” with “growth”, according to the history of inputs of spur and commands from a user and the history of its own behavior and actions, as if a real animal makes his behavior according to how to be raised by his owner. 
     (2) Processing by Controller  2   
     Next specific processing by a controller  10  in the pet robot  1  will be explained. 
     As shown in FIG. 4, the contents of processing by the controller  2  are functionally divided into five sections: a state recognition mechanism section  30  for recognizing the external and internal states; a emotion/instinct model section  31  for determining the state of emotion and instinct based on the recognition result obtained by the state recognition mechanism section  30 ; a behavior determination mechanism section  32  for determining next behavior and action based on the recognition result obtained by the state recognition mechanism section  30  and the output of the emotion/instinct model section  31 ; a posture transition mechanism section  33  for making a motion plan as to how to make the pet robot  1  to perform the behavior and action determined by the action determination mechanism section  32 ; and a device control mechanism section  34  for controlling the actuators  21   1  to  21   n  based on the motion plan made by the posture transition mechanism section  33 . 
     Hereinafter, the state recognition mechanism section  30 , the emotion/instinct model section  31 , the behavior determination mechanism section  32 , the posture transition mechanism section  33 , the device control mechanism section  34  and the growth control mechanism section  35  will be explained. 
     (2-1) Operation of State Recognition Mechanism Section  30   
     The state recognition mechanism section  30  recognizes the specific state based on the external information signal S 1  given from the external sensor section  19  (FIG. 2) and the internal information signal S 2  given from the internal sensor section  15 , and gives the emotion/instinct model section  31  and the behavior determination mechanism section  32  the recognition result as state recognition information S 10 . 
     In actual, the state recognition mechanism section  30  always checks the audio signal S 1 A which is given from the microphone  16  (FIG. 2) of the external sensor section  19 , and when detecting that the spectrum of the audio signal S 1 A has the same scales as a command sound which is outputted from the sound commander for a command such as “walk”, “lie down” or “chase a ball”, recognizes that the command has been given, and gives the recognition result to the emotion/instinct model section  31  and the behavior detection mechanism section  32 . 
     Further, the state recognition mechanism section  30  always checks the video signal S 1 B which is given from the CCD camera  17  (FIG.  2 ), and when detecting “something red” or “a plane which is perpendicular to the ground and is higher than a prescribed height” in the picture based on the video signal S 1 B, recognizes that “there is a ball” or “there is a wall”, and then gives the recognition result to the emotion/instinct model section  31  and the behavior determination mechanism section  32 . 
     Furthermore, the state recognition mechanism section  30  always checks the pressure detection signal S 1 C which is given from the touch sensor  18  (FIG.  2 ), and when detecting pressure having a higher value than a predetermined threshold value, for a short time (less than two seconds, for example), based on the pressure detection signal S 1 C, recognizes that “it was hit (scold)”, and on the other hand, when detecting pressure having a lower value than a predetermined threshold, for a long time (two seconds or more, for example), recognizes that “it was stroked (praised)”. Then, the state recognition mechanism section  30  gives the recognition result to the emotion/instinct model section  31  and the behavior determination mechanism section  32 . 
     Furthermore, the state recognition mechanism section  30  always checks the acceleration detection signal S 2 C which is given from the acceleration sensor  14  (FIG. 2) of the internal sensor section  15 , and when detecting the acceleration having a higher level than a preset predetermined level, based on the acceleration signal S 2 C, recognizes that “it received a big shock”, or when detecting the bigger acceleration like acceleration by gravitation, recognizes that “it fell down (from a desk or the like)”. And then the state recognition mechanism section  30  gives the recognition result to the emotion/instinct model  31  and the behavior determination mechanism section  32 . 
     Furthermore, the state recognition mechanism section  30  always checks the temperature detection signal S 2 B which is given from the thermal sensor  13  (FIG.  2 ), and when detecting a temperature higher than a predetermined level, based on the temperature detection signal S 2 B, recognizes that “the internal temperature has increased” and then gives the recognition result to the emotion/instinct model section  31  and the behavior determination mechanism section  32 . 
     (2-2) Operation by Feeling/Instinct Model Section  31   
     The emotion/instinct model section  31 , as shown in FIG. 5, has a group of basic emotions composed of emotional units  40 A to  40 F as emotion models corresponding to six emotions of “joy”, “sadness”, “surprise”, “horror”, “hate” and “anger”, a group of basic desires  41  composed of desire units  41 A to  41 D as desire models corresponding to four desires of “appetite”, “affection”, “exploration” and “exercise”, and strength fluctuation functions  42 A to  42 H corresponding to the emotional units  40 A to  40 F and desire units  41 A to  41 D. 
     For example, each emotional unit  40 A to  40 F expresses the strength of the corresponding emotion by its strength ranging from level 0 to 100, and changes the strength based on the strength information A 11 A to A 11 F which is given from the corresponding strength fluctuation function  42 A to  42 F, time to time. 
     Similarly to the emotional units  40 A to  40 F, each desire unit  41 A to  41 D expresses the strength of the corresponding desire by a level ranging from 0 to 100, and changes the strength based on the strength information S 12 G to S 12 F which is given from the corresponding strength fluctuation function  42 G to  42 K, time to time. 
     Then, the emotion/instinct model section  31  determines the emotion by combining the strengths of these emotional units  40 A to  40 F, and also determines the instinct by combining the strengths of these desire units  41 A to  41 D and then outputs the determined emotion and instinct state to the behavior determination mechanism section  32  as emotion/instinct state information S 12 . 
     Note that, the strength fluctuation functions  42 A to  42 G are functions to generate and output the strength information S 11 A to A 11 G for increasing or decreasing the strengths of the emotional units  40 A to  40 F and the desire units  41 A to  41 D according to the preset parameters as described above, based on the state recognition information S 10  which is given from the state recognition mechanism section  30  and the behavior information S 13  indicating the current or past behavior of the pet robot  1  himself which is given from the behavior determination mechanism section  32  which will be described later. 
     Under this operation, the pet robot  1  can have his characters such as “aggressive” or “shy” by setting the parameters of these strength fluctuation functions  42 A to  42 G to different values for each behavior and action model (Baby 1, Child 1, Child 2, Young 1 to Young 3, Adult 1 to Adult 4). 
     (2-3) Operation of Behavior Determination Mechanism Section  32   
     The behavior determination mechanism section  32  has a plurality of behavior models for each behavior and action model (Baby 1, Child 1, Child 2, Young 1 to Young 3, and Adult 1 to Adult 4) in a memory  10 A. 
     Based on the state recognition information S 10  given from the state recognition mechanism section  30 , the strengths of the emotional units  40 A to  40 F and desire units  41 A to  41 D of the emotion/instinct model section  31 , and corresponding behavior models, the behavior determination mechanism section  32  determines next behavior and action, and outputs the determination result as behavior determination information S 14  to the posture transition mechanism section  33 . 
     At this point, as a technique of determining next behavior and action, the behavior determination mechanism section  32  uses an algorithm called a probability automaton which is to probability determine that transition is made from one node (state) ND A0  to which node ND A0  to ND An , the same or another, based on transition probability P 0  to P n  set for arcs AR A0  to AR An  connecting between the nodes ND A0  to ND An , as shown in FIG.  6 . 
     More specifically, the memory  10 A has stored a state transition table  50  as shown in FIG. 7 as behavior models for each node ND A0  to ND An , so that the behavior determination mechanism section  32  determines next behavior and action based on this state transition table  50 . 
     In this state transition table  50 , input events (recognition results) which are conditions for transition from a node ND A0  to ND An  are shown in a priority order in a line of “input event name” and further conditions for the transition conditions are shown in the same rows of the lines of “data name” and “data range”. 
     With respect to the node ND 100  defined in the state transition table  50  of FIG. 7, in the case where the recognition result of “detect a ball” is obtained, or in the case where the recognition result of “detect an obstacle” is obtained, a condition to make a transition to another node is that the “size” of the ball which is information given together with the recognition result is “between 0 to 1000 (0, 1000)”, or that the “distance” to the obstacle which is information given together with the recognition result is “between 0 to 100 (0, 100)”. 
     In addition, if there is no recognition result input, transition can be made from this node ND 100  to another node when the strength of any emotional unit  40 A to  40 F out of the “joy”, “surprise” or “sadness” is “between 50 and 100 (50, 100), out of the strengths of the emotional units  40 A to  40 F and the desire units  41 A to  41 D which are periodically checked by the behavior determination mechanism section  32 . 
     In addition, in the state transition table  50 , the names of nodes to which a transition can be made from the node ND A0  to ND An  are shown in a row of a “transition destination node” in a column of “transition probability to another node”, and transition probability to another node ND A0  to ND An  at which transition can be made when the conditions shown in the “input event name”, “data name” and “data range” are all met, are shown in a row of “output behavior” in the column of “transition probability to another node”. It should be noted that the sum of transition probability in each row in the column of “transition probability to another node” is 100% 
     Therefore, with respect to this example of node NODE 100 , in the case where “a ball (BALL) is detected” and the recognition result indicating that the “size” of the ball is “between 0 to 1000 (0, 1000) is obtained, a transition can be made to “node NODE 120  (node  120 )” at probability of “30%”, and at this point, the behavior and action of “ACTION 1” are to be output. 
     Each behavior model is composed of the nodes ND A0  to ND An , which are shown by such state transition table  50 , connected one to others. 
     As described above, the behavior determination mechanism section  32 , when receiving the state recognition information S 10  from the state recognition mechanism section  30 , or when a predetermined time passes after the last action is performed, probably determines next behavior and action (behavior and action shown in the row of “output behavior”) by referring to the state transition table  50  relating to the node ND A0  to ND An  corresponding to the corresponding behavior model stored in the memory  10 A. 
     (2-4) Processing by Posture Transition Mechanism Section  33   
     The posture transition mechanism section  33 , when receiving the behavior determination information S 14  from the behavior determination mechanism section  32 , makes a motion plan for a series of actions as to how to make the pet robot  1  perform the behavior and action based on the behavior determination information S 14 , and then gives the device control mechanism section  34  action order information S 15  based on the motion plan. 
     At this point, the posture transition mechanism section  33 , as a technique to make a motion plan, uses a directed graph as shown in FIG. 8 where postures the pet robot  1  can take are taken to as nodes ND B0  to ND B2 , the nodes N B0  to ND B2  between which the transition can be made are connected with directed arcs AR B0  to AR B2  indicating actions, and each action which can be performed while the action of a node ND B0  to ND B2  is performed is taken to as a self action arc AR C0  to AR C2 . 
     (2-5) Processing by Device Control Mechanism Section  34   
     The device control mechanism section  34  generates a control signal S 16  based on the action order information S 15  which is given from the posture transition mechanism section  33 , and drives and controls each actuators  21   1  to  21   n  based on the control signal S 16 , to make the pet robot  1  perform designated behavior and action. 
     (2-6) Awakening Level and Interaction Level 
     This pet robot  1  has a parameter called an awakening level indicating the awakening level of the pet robot  1  and a parameter called an interaction level indicating how often a user, an owner, made spurs, so as to adapt the life pattern of the pet robot  1  to the life pattern of the user. 
     The awakening level parameter is a parameter which allows the behavior and emotion of the robot or the tendency of behavior to be executed, to have a certain rhythm (cycle). For example, such tendency may be created that dull behavior is to be made in the morning when the awakening level is low and lively behavior is to be made in the evening when the awakening level is high. This rhythm corresponds to the biorhythm of human beings and animals. 
     In this description, the awakening level parameter is used but another word can be used such as a biorhythm parameter, as long as it is a parameter which occurs the same results. In this embodiment, the value of the awakening level parameter is increased when the robot starts. However, a fixed temporal fluctuation cycle may be preset for the awakening level parameter. 
     With respect to this awakening level, 24 hours in a day are divided by a predetermined time period, 30 minutes for example, which is called a time slot, to divide the 24 hours into 48 time slots, an awakening level is expressed by a level ranging from 0 to 100 for each time slot and is stored in the memory  10 A of the controller  10  as an awakening parameter table. In this awakening parameter table, the same awakening level is set to all time slots as an initial value as shown in FIG.  9 (A). 
     When the user turns on the power of the pet robot  1  to drive under this state, the controller  10  increases the awakening levels of the time slot of time when the pet robot  1  starts and of the time slots around that time by predetermined levels, and at the same time, equally divides and decreases the total of the added awakening levels from the awakening levels of the other time slots, and then updates the awakening parameter table. 
     In this way, while the user repeatedly starts and uses the pet robot  1 , the controller  10  regulates the total of awakening levels of time slots so as to create the awakening parameter table suitable for the life pattern of the user. 
     That is, when the user starts the pet robot  1  by turning its power on, the controller  10  executes an awakening parameter table creating processing procedure RT 1  shown in FIG.  10 . The state recognition mechanism section  30  of the controller  10  starts the awakening parameter table creating processing procedure RT 1  of FIG. 10, and at step SP 1 , recognizes that the pet robot  1  has started, based on the internal information signal S 2  given from the internal sensor section  15 , and gives this recognition result as state recognition information S 10  to the emotion/instinct model section  31  and the behavior determination mechanism section  32 . 
     The emotion/instinct model section  31 , when receiving the state recognition information S 10 , takes the awakening parameter table out of the memory  10 A, moves to step  2  where it judges whether the current time Tc is multiple of the detection time Tu for detecting the drive state of the pet robot  1 , and repeats the processing step SP 2  until an affirmative result is obtained. The period between two successive detection times Tu has been selected so as to be much shorter than the time period of the time slot. 
     When an affirmative result is obtained at step SP 2 , this means that the detection time Tu for detecting the drive state of the pet robot  1  has just come, and in this case, the emotion/instinct model section  31  moves to step SP 3  to add “a” levels (2 levels, for example) to the awakening level awk[i] of i-th time slot to which the current time Tc belongs, and also to add “b” levels (1 level, for example) to the awakening levels awk[i-1] and awk[i+1] of the time slots which exist before and after the i-th time slot. 
     However, if the addition result exceeds level  100 , an awakening level awk is compulsory set to level  100 . As described above, the emotion/instinct model section  31  adds a predetermined level to the awakening levels of time slots around the time when the pet robot  1  is active, thereby preventing the awakening level awk [i] of only one time slot from projecting and increasing. 
     Then, at step SP 4 , the emotion/instinct model  31  calculates the total (a+2b) of the added awakening levels awk as Δawk, and moves to following step SP 5  where it subtracts Δawk/(N−3) from each of the levels starting with the awakening level awk[1] of the first time slot to the awakening level awk[i−2] of the (i−2)-th time slot and each of the levels starting with from the awakening level awk[i+2] of the (i+2)-th time slot to the awakening level awk[48] of the 48th time slot. 
     At this point, if a subtraction result is less than level 0, the awakening level awk is compulsory set to level 0. The emotion/instinct model section  31  equally divides and subtracts the total Δawk of the added awakening level from all awakening levels awk of the time slots other than the increased time slots, as described above, thereby keeping a balance of the awakening parameter table by regulating the total of the awakening levels awk in a day. 
     Then, at step SP 6 , the emotion/instinct model section  31  gives the behavior determination mechanism section  32  the awakening level awk of each time slot in the awakening parameter table, to reflect the value of each awakening level awk in the awakening parameter table on the behavior of the pet robot  1 . 
     Specifically, when the awakening level awk is high, the emotion/instinct model section  31  does not greatly decrease the level of desire of the desire unit  41 D of “exercise” even the pet robot  1  exercises very hard, and on the other hand, when the awakening level awk is low, the emotion/instinct model section  31  immediately decreases the level of desire of the desire unit  41 D of “exercise” after little exercise, and in this way, it indirectly changes the activity based on the level of desire of the desire unit  41 D of “exercise” according to the awakening level awk. 
     On the other hand, as to the selection of a node in the state transition table  50 , the behavior determination mechanism section  32  increases the transition probability for making a transition to an active node when the awakening level awk is high, and decreases the transition probability for making a transition to an active node when the awakening level awk is low, thus it directly changes the activity according to the awakening level awk. 
     Therefore, when the awakening level awk is low, the behavior determination mechanism section  32  selects a node so as to express a sleepiness state through “yawn”, “lie down” or “stretch”, at high possibility in the state transition table  50 , in order to directly express that the pet robot  1  is sleepy, to the user. If the awakening level awk given from the emotion/instinct mode section  31  is lower than a predetermined threshold value, the behavior determination mechanism section  32  shuts the pet robot  1  down. 
     Then the emotion/instinct model section  31  moves to following step SP 7  to judge whether the pet robot  1  has been shut down, and then repeats the aforementioned steps SP 2  to SP 6  until an affirmative result is obtained. 
     When an affirmative result is obtained at step SP 6 , this means that the awakening level awk is lower than a predetermined threshold value (a lower value is selected than an initial value of the awakening level awk in this case) shown in FIGS.  9 (A) and  9 (B), or that the user turns the power off, then the emotion/instinct model section  31  moves to following step SP 8  to store the values of the awakening level awk[1] to awk[48] in the memory  10 A in order to update the awakening parameter table and then, moves to step SP 9  where the processing procedure RT 1  is terminated. 
     At this point, the controller  10  refers to the awakening parameter table stored in the memory  10 A to detect time corresponding to a time slot of which the awakening level awk becomes larger than a threshold value and to perform various setting so as to restart the pet robot  1  at the detected time. 
     As described above, the pet robot  1  starts when the awakening level becomes higher than a predetermined threshold value and on the other hand, shuts down when the awakening level becomes lower than a predetermined threshold value, thereby the pet robot  1  can naturally wake and sleep according to the awakening level awk, thus making it possible to adapt the life pattern of the pert robot  1  to the life pattern of the user. 
     In addition, the pet robot  1  has a parameter called an interaction level indicating how often the user made spurs, and a time-passage-based averaging method is used as a method of obtaining this interaction level. 
     For the time-passage-based averaging method, inputs through user&#39;s spurs are selected out of inputs to the pet robot  1  at first, and then points which have been decided in correspondence with the kinds of spurs are stored in the memory  10 A. That is, each spur from the user is converted into a numerical value which is stored in the memory  10 A. In this pet robot  1 ,  15  points for “call name”, 10 points for “stroke head”, 5 points for “touch switch of head or the like”, 2 points for “hit”, and 2 points for “hold up” are set and stored in the memory  10 A. 
     The emotion/instinct model section  31  of the controller  10  judges based on the state recognition information S 10  given from the state recognition mechanism section  30  whether the user has made a spur. When it is judged that the user has made a spur, then the emotion/instinct model section  31  stores the number of points corresponding to the spur and time. Specifically, the emotion/instinct model section  31  sequentially stores  5  points at 13:05:30, 2 points at 13:05: 10 and 10 points at 13:08:30, and sequentially deletes data which has been stored for a fixed time (15 minutes, for example). 
     In this case, the emotion/instinct model section  31  previously sets a time period (10 minutes, for example) for calculating an interaction level, and calculates the total of points which exist from the set time period before the present time to the present time, as shown in FIG.  11 . Then the emotion/instinct model section  31  normalizes the calculated points to be within a preset range and takes this normalized points to as the interaction level. 
     Then, as shown in FIG. 9C, the emotion/instinct model section  31  adds the interaction level to the awakening level of time slot corresponding to the time period when the aforementioned interaction level is obtained, and gives it to the behavior determination mechanism section  32 , so that the interaction level can reflect on behavior of the pet robot  1 . 
     Thereby, even if the pet robot  1  has an awakening level lower than the predetermined threshold value, when the value obtained by adding the interaction level to the awakening level becomes higher than the threshold value, the pet robot  1  starts and stands up so as to communicate with the user. 
     On the contrary, if the value obtained by adding the interaction level to the awakening level becomes lower than the threshold value, the pet robot  1  is shut down. In the case, the pet robot  1  detects time corresponding to the time slot where a value which is obtained by adding the interaction level to the awakening level becomes higher than the threshold value, by referring to the awakening parameter table stored in the memory  10 A, and performs various settings so that the pet robot  1  restarts at that time. 
     As described above, the pet robot  1  starts when the value obtained by adding the interaction level to the awakening level becomes higher than a predetermined threshold value, while it shuts down when the value obtained by adding the interaction level to the awakening level becomes lower than a predetermined threshold value, thereby it can wake up and sleep naturally according to the awakening level and further, even the awakening level is low, the interactive level is increased by the user&#39;s spurs, which wakes the pet robot  1  up, and therefore, the pet robot  1  can sleep and wake up more naturally. 
     Further, the behavior determination mechanism section  32  increases transition probability for making a transition to an active node when the interaction level is high, while it increases transition probability for making a transition to an inactive node when the interaction level is low, thus making it possible to change activity of behavior according to the interaction level. 
     As a result, when a node is selected from the state transition table  50 , the behavior determination mechanism section  32  selects behavior such as dancing, singing or big performance which a user should see, at high probability when the interaction level is high, while selecting behavior such as awakening, exploring or playing with an object which a user may not see, at high probability when the interaction level is low. 
     At this point, in the case where the interaction level becomes lower than a threshold value, the behavior determination mechanism section  32  is to save consumption energy by turning the power of unnecessary actuators  21 , decreasing gains of the actuators  21  or lying down, for example, and further, is to reduce the controller&#39;s  10  loads by stopping the audio recognition function. 
     (3) Operation and Effects of the Present Embodiment 
     The controller  10  of the pet robot  1  creates the awakening parameter table indicating the awakening level of the pet robot  1  for each time zone in a day, by starting and shutting down repeatedly, and stores it in the memory  10 A. 
     Then, the controller  10  refers to the awakening parameter table, and shuts down when the awakening level is lower than a predetermined threshold value and at this point, sets a timer for the time when the awakening level becomes higher next, to restart, so that the life rhythm of the pet robot  1  can be adapted to the life rhythm of a user. Thus the user can communicate more easily and get a larger sense of affinity. 
     When the user makes a spur, the controller  10  calculates the interaction level indicating the frequency of spurs, and adds the interaction level to corresponding awakening level in the awakening parameter table. Thereby, even in the case where the awakening level is lower than a predetermined threshold value, the controller  10  starts and stands up when the total of the awakening level and the interactive level becomes higher than the threshold value and as a result, communication can be performed with a user and the user can get a larger sense of affinity. 
     According to the aforementioned operation, the pet robot  1  can start and shut down according to the history of use of the pet robot  1  by a user, thus making it possible to adapt the life rhythm of the pet robot  1  to the life rhythm of the user, so that the user can get a larger sense of affinity and entertainment property can be improved. 
     (4) Other Embodiments 
     Note that, in the aforementioned embodiment, the total Δ awk of the added awakening levels is equally divided and subtracted from all awakening levels of time slots other than the increased time slots. This present invention, however, is not limited to this and as shown in FIG. 12, the awakening levels of time slots after a predetermined time may be partly reduced for the increased time slots. 
     Further, in the aforementioned embodiment, the threshold value which is a standard of start or shut-down is selected to be a lower value than the initial value of the awakening level awk. The present invention is not limited to this and as shown in FIG. 12, another value can be selected to be a higher value than the initial value of awakening level awk. 
     Further, in the aforementioned embodiment, the pet robot  1  starts and shuts down based on the awakening parameter table which changes according to the history of use of the pet robot  1  by a user. The present invention, however, is not limited to this and a fixed awakening parameter table which is created based on the age and characters of the pet robot  1  may be utilized. 
     Furthermore, in the aforementioned embodiment, the time-passage-based averaging method is applied to the calculation method of interaction levels. This present invention, however, is not limited to this and another method may be applied, such as a time-passage-based average weighting method or a time-based subtracting method. 
     In the weighting method by time-passage-based average, with the present time as a basis, higher weighting coefficients are selected for newer inputs, while lower weighting coefficients are selected for older inputs. For example, with the present time as a basis, the weighting coefficients are set: 10 for inputs before 2 minutes or less; 5 for inputs between 5 minutes before and 2 minutes before; and 1 for inputs between 10 minutes before and 5 minutes before. 
     Then, the emotion/instinct model section  31  multiplies points of each spur which exists from time which is a predetermined time before the present time to the present time, by the corresponding weighting coefficient, and calculates the total to obtain the interaction level. 
     In addition, the time-based subtracting method is for obtaining an interaction level by using a variable called an internal interaction level. In this case, when a user makes a spur, the emotion/instinct model section  31  adds points corresponding to the kind of spur to the internal interaction level. At the same time, the emotion/instinct model section  31  decreases the internal interaction level as time passes, by, for example, multiplying the previous internal interaction level by 0.1 every time when one minute passes. 
     Then, when the internal interaction level becomes lower than a predetermined threshold value, the emotion/instinct model section  31  takes the internal interaction level to as the aforementioned interaction level, while it takes the threshold value to as the interaction level when the internal interaction level becomes higher than the threshold value. 
     Back to the aforementioned embodiment, a combination of the awakening parameter table and the interaction level is applied to the history of use. This present invention, however, is not limited to this and another kind of history of use which indicates a history of user use in a temporal axis direction may be applied. 
     Furthermore, in the aforementioned embodiment, the memory  10 A is utilized as a storage medium. This present invention, however, is not limited to this and the history of user use may be stored in another kind of storage medium. 
     Furthermore, in the aforementioned embodiment, the controller  10  is utilized as a behavior determination means. The present invention is not limited to this and another kind of behavior determination means can be utilized to determine next behavior according to the history of use. 
     Furthermore, the aforementioned embodiment is applied to a four-legged walking robot which is constructed as shown in FIG.  1 . This present invention, however, is not limited to this and may be applied to another kind of robot. 
     Industrial Utilization 
     The present invention can be applied to a pet robot, for example.