Method and device for socially aware model predictive control of a robotic device using machine learning

A control unit configured to determining a control trajectory for a robotic device, the control unit configured to:

• perform an information theoretic model predictive control applying a control trajectory sample prior in time steps to obtain the control trajectory for a given time horizon;
• determine the control trajectory sample prior depending on a data driven trajectory prediction model which is trained to output a control trajectory sample as the control trajectory sample prior based on an actual state of the robotic device;
• wherein the control trajectory sample prior is obtained by a modelled control trajectory sample obtained using the trajectory prediction model and the control trajectory sample obtained in a last time step;
• wherein the control trajectory sample prior is obtained as a sum of the modelled control trajectory sample obtained by the trajectory prediction model and the control trajectory sample obtained in a last time step, each weighted according to their trajectory costs according to a given cost function;
• wherein the modelled control trajectory sample is only considered when the trajectory costs of the modelled control trajectory sample are higher than the trajectory costs of the control trajectory sample obtained in the last time step.

A robotic device, comprising:

• an actuation unit configured to move the robotic device according to a control trajectory;
• a control unit configured to:
• perform an information theoretic model predictive control applying a control trajectory sample prior in time steps to obtain the control trajectory for a given time horizon;
• determine the control trajectory sample prior depending on a data driven trajectory prediction model which is trained to output a control trajectory sample as the control trajectory sample prior based on an actual state of the robotic device;
• wherein the control trajectory sample prior is obtained by a modelled control trajectory sample obtained using the trajectory prediction model and the control trajectory sample obtained in a last time step;
• wherein the control trajectory sample prior is obtained as a sum of the modelled control trajectory sample obtained by the trajectory prediction model and the control trajectory sample obtained in the last time step, each weighted according to their trajectory costs according to a given cost function;
• wherein the modelled control trajectory sample is only considered when the trajectory costs of the modelled control trajectory sample are higher than the trajectory costs of the control trajectory sample obtained in the last time step.

A non-transitory machine readable medium on which are stored instructions for determining a control trajectory for a robotic device, the instructions, when executed by a computer, causing the computer to perform the following steps:

• performing an information theoretic model predictive control applying a control trajectory sample prior in time steps to obtain the control trajectory for a given time horizon;
• determining the control trajectory sample prior depending on a data-driven trajectory prediction model which is trained to output a control trajectory sample as the control trajectory sample prior based on an actual state of the robotic device;
• wherein the control trajectory sample prior is obtained by a modelled control trajectory sample obtained using the trajectory prediction model and the control trajectory sample obtained in a last time step;
• wherein the control trajectory sample prior is obtained as a sum of the modelled control trajectory sample obtained by the trajectory prediction model and the control trajectory sample obtained in a last time step, each weighted according to their trajectory costs according to a given cost function;
• wherein the modelled control trajectory sample is only considered when the trajectory costs of the modelled control trajectory sample are higher than the trajectory costs of the control trajectory sample obtained in the last time step.