Reinforcement learning for contact-rich tasks in automation systems
AssigneesSiemens Aktiengesellschaft, The Regents of the University of California
InventorsEugen Solowjow, Juan L. Aparicio Ojea, Chengtao Wen, Jianlan Luo
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Abstract
Systems and methods for controlling robots including industrial robots. A method includes executing (402) a program (550) to control a robot (102) by the robot control system (120, 500). The method includes receiving (404) robot state information (554). The method includes receiving (406) force torque feedback (556) inputs from a sensor (554) on the robot (102). The method includes producing (410) a robot control command for the robot (102) based on the robot state information (554) and the force torque feedback (556) inputs. The method includes controlling (412) the robot (102) using the robot control command.
Claims8
A process performed by a robot control system, comprising:
- executing a program to control a robot by the robot control system;
- receiving robot state information by the robot control system;
- receiving force torque feedback inputs from a sensor on the robot by the robot control system;
- producing a robot control command for the robot using a guided policy search process, by the robot control system, based on the robot state information and the force torque feedback inputs and on a reference signal of a neural network, wherein the reference signal is based on the robot state information and the force torque feedback inputs, the force torque feedback inputs being added to a next-to-last layer of the neural network at force torque information nodes;
- and
- controlling the robot using the robot control command, by the robot control system.
Dependencies are inferred from the claim text.