Controlling a robot based on an optimized cooperation with other agents

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;

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.

2 . 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.

3 . 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.

4 . 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.

5 . 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.

6 . The method according to claim 2 , 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.

7 . 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.

8 . 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.

9 . 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.

10 . 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.

11 . 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.

12 . 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.

13 . 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.

14 . 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.

15 . 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.

16 . 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.

17 . The method according to claim 1 , wherein

the at least two agents comprise plural autonomous devices.