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
Life of this patent
Estimating the term…
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.
Claims9
A process (400) performed by a robot control system (120, 500), comprising:
- executing (402) a program (550) to control a robot (102) by the robot control system (120, 500);
- receiving (404) robot state information (554) by the robot control system (120, 500);
- receiving (406) force torque feedback (556) inputs from a sensor (554) on the robot (102) by the robot control system (120, 500);
- producing (410) a robot control command for the robot (102), by the robot control system (120, 500), based on the robot state information (554) and the force torque feedback (556) inputs;
- and
- controlling (412) the robot (102) using the robot control command, by the robot control system (120, 500).
Dependencies are inferred from the claim text.