# US 12,759,149 B2 · Controlling a robot based on an optimized cooperation with other agents > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,759,149 B2 - Document ID: US12759149B2 - Status: Granted - Kind: B2 - Application number: 18/192,667 - Filed: 2023-03-30 - Published: 2023-11-02 - Granted: 2026-10-13 - Earliest claimed: 2022-03-31 - Assignees: Honda Motor Co., Ltd. - Applicants: Honda Motor Co., Ltd. - Inventors: Patricia Wollstadt; Christiane Wiebel-Herboth - Web page: https://patentgrep.com/patent/US12759149B2 ## CPC - B25J 9/1669: {characterised by special application, e.g. multi-arm co-operation, assembly, grasping} - B25J 13/08: by means of sensing devices, e.g. viewing or touching devices - G05B 19/4155: characterised by program execution, i.e. part program or machine function execution, e.g. selection of a program - G06F 3/015: {Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection} - G05B 2219/50391: Robot - B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50}) - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) ## Abstract A method for controlling at least one autonomous device which is one of at least two agents that cooperatively perform a common task in a common environment is provided. The method comprises: obtaining variables on a current state of each agent in the common environment, and obtaining a further variable on a current state of the common environment that describes a distance of the current state of the agents to a common goal state or a task success; determining a quantitative measure for cooperative behaviour of the agents that quantifies an extent to which a mutual support or an adaption in joint cooperative actions towards the common goal state increases a joint action space of the agents; optimizing a joint behaviour of the agents using the quantitative measure based on the obtained variables to determine an action of the autonomous device; and outputting a control signal for controlling the action. ## Claims ### Claim 1 Method for controlling at least one autonomous device, which is at least one of at least two agents that cooperatively perform a common task in a common environment, the method comprising: obtaining variables on a current state of each of the at least two agents in the common environment; obtaining a further variable on at least one current state of the common environment, wherein the variable describes a distance of the current state of the at least two agents to a common goal state or a current task success; determining a quantitative measure for cooperative behaviour of the at least two agents, wherein the quantitative measure quantifies an extent to which a mutual support or an adaption in joint cooperative actions towards the common goal state increases a joint action space of the at least two agents, wherein determining the quantitative measure applies an information-theoretic partial information decomposition (PID) framework to quantify a synergistic contribution of actions of the at least two agents to the common goal state or the current task success; optimizing a joint behaviour of the at least two agents using the determined quantitative measure based on the obtained variables of the current states of the at least two agents and the obtained further variable on the current state of the common environment; determining at least one action of the autonomous device based on the optimized joint behaviour; and outputting a control signal for controlling the determined at least one action. ### Claim 2 (depends on 1) The method according to claim 1, wherein obtaining the variables includes obtaining at least one first variable that describes an action of the at least one agent, and at least one second variable that describes an action of the autonomous device, and obtaining the further variable includes obtaining at least one third variable that describes the current state of the common environment or of the at least two agents with respect to a target state of the common environment. ### Claim 3 (depends on 2) The method according to claim 2, wherein the at least one first variable includes at least one of measured biophysical signals, in particular electromyography signals, predictive gazing and automatic imitation signals measured by eye tracking and EMG, galvanic skin conductance and ocular activity signals, force of motion signals measured from motor activity of the at least one agent, poses of the agent estimated based on images acquired by the autonomous device, and trajectories calculated based on motion tracking. ### Claim 4 (depends on 2) The method according to claim 2, wherein the at least one second variable includes at least one of joint positions, motion trajectories, forces and velocities of the autonomous device. ### Claim 5 (depends on 2) The method according to claim 2, wherein the at least one third variable includes at least one of a measure of the current task success, a distance to the target state of the common environment, in particular an Euclidian distance to the target state of the common environment, and a first derivative of the distance. ### Claim 6 (depends on 2) The method according to claim 2, wherein determining the quantitative measure applies the information-theoretic partial information decomposition (PID) framework to the first and second variables as input variables and the third variable as output variable, in particular applies the PID framework to quantify the synergistic contribution of the first and second variables as input variables and the third variable as output variable. ### Claim 7 (depends on 2) The method according to claim 2, wherein determining the quantitative measure applies a linear model to quantify a synergistic contribution of the first and second variables as input variables and the third variable as output variable. ### Claim 8 (depends on 6) The method according to claim 6, wherein in addition to the quantitative measure quantifying the synergistic contribution of the first and second variables as input variables and the third variable as output variable, optimizing the joint behaviour of the at least two agents uses a distance to the target state as a constraint or uses a derivative of the distance having a negative value as a constraint or maximizes the measure of the current task success as a constraint. ### Claim 9 (depends on 7) The method according to claim 7, wherein in addition to the quantitative measure quantifying the synergistic contribution of the first and second variables as input variables and the third variable as output variable, optimizing the joint behaviour of the at least two agents uses a distance to the target state as a constraint or uses a derivative of the distance having a negative value as a constraint or maximizes the measure of the current task success as a constraint. ### Claim 10 (depends on 1) The method according to claim 1, wherein optimizing the joint behaviour of the at least two agents comprises optimizing a future target state of the at least one autonomous device by applying an optimization algorithm using the determined quantitative measure, and applying a trajectory planning strategy to generate a trajectory to the optimized future target state. ### Claim 11 (depends on 10) The method according to claim 10, wherein applying the optimization algorithm using the determined quantitative measure comprises applying a gradient-based optimization algorithm on the determined quantitative measure, in particular applying gradient-based descent, or a non-gradient-based optimization algorithm, in particular an evolutionary algorithm, or a Bayesian optimization algorithm. ### Claim 12 (depends on 1) The method according to claim 1, wherein optimizing the joint behaviour of the at least two agents comprises determining at least one action of the at least one autonomous device by applying a trajectory planning strategy, in particular a trajectory planning strategy for co-manipulation scenarios, to generate a trajectory to an optimized future target state. ### Claim 13 (depends on 1) The method according to claim 1, wherein optimizing the joint behaviour of the at least two agents comprises using the determined quantitative measure directly in an objective function or as a constraint during optimizing at least one motion trajectory to generate a trajectory to an optimized future target state, in particular by a sampling based motion planning (SBMP) process or a trajectory optimization framework. ### Claim 14 (depends on 13) The method according to claim 13, wherein optimizing the joint behaviour of the at least two agents comprises achieving the optimized future target state using the SBMP process, by identifying trajectories between a start state and a target state of the at least one autonomous device, assigning cost to the identified trajectories based on a metric, wherein the cost comprises an element of cooperation between the at least two agents based on the determined quantitative measure, and selecting a trajectory with minimized cost for achieving the optimized future target state. ### Claim 15 (depends on 1) The method according to claim 1, wherein determining the quantitative measure for cooperative behaviour of the at least two agents comprises estimating the quantitative measure based on acquired data of actions or motions of the at least two agents and a corresponding target state, in particular based on the acquired data from past instances of performing the task, from observing humans interacting in performing the task, or from sampling a joint action space of the at least two agents. ### Claim 16 (depends on 1) The method according to claim 1, wherein obtaining the variables on the current state of each of the at least two agents in the common environment includes obtaining predictions on the at least two agents based on a known internal policy of the at least two agents or a past behaviour of the at least two agents. ### Claim 17 (depends on 1) The method according to claim 1, wherein the at least two agents comprise plural autonomous devices. ### Claim 18 Method for controlling at least one autonomous device, which is at least one of at least two agents that cooperatively perform a common task in a common environment, the method comprising: obtaining variables on a current state of each of the at least two agents in the common environment, wherein obtaining the variables includes obtaining at least one first variable that describes an action of the at least one agent, and at least one second variable that describes an action of the autonomous device, wherein the at least one second variable includes at least one of joint positions, motion trajectories, forces and velocities of the autonomous device; obtaining a further variable on at least one current state of the common environment, wherein the variable describes a distance of the current state of the at least two agents to a common goal state or a current task success, wherein obtaining the further variable includes obtaining at least one third variable that describes the current state of the common environment or of the at least two agents with respect to the target state of the common environment; determining a quantitative measure for cooperative behaviour of the at least two agents, wherein the quantitative measure quantifies an extent to which a mutual support or an adaption in joint cooperative actions towards the common goal state increases a joint action space of the at least two agents, wherein determining the quantitative measure applies an information-theoretic partial information decomposition (PID) framework to the first and second variables as input variables and the third variable as output variable, in particular applies the PID framework to quantify a synergistic contribution of the first and second variables as input variables and the third variable as output variable; optimizing a joint behaviour of the at least two agents using the determined quantitative measure based on the obtained variables of the current states of the at least two agents and the obtained further variable on the current state of the common environment; determining at least one action of the autonomous device based on the optimized joint behaviour; and outputting a control signal for controlling the determined at least one action.