Robot control policy
AssigneeMentee Robotics LTD.
InventorsShir GUR, Tom SHENKAR, Shahar LUTATI
Life of this patent
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Abstract
There is provided a method for learning a bipedal robot control policy, the method includes (i) learning, by a processing circuit, an action-related corrective policy that once applied reduces a gap associated with an initial simulation state transition function and with a real world state transition function; and (ii) determining a control policy of the bipedal robot in a simulator, using the action-related corrective policy.
Claims20
A method for learning a robot control policy, the method comprising:
- learning using reinforcement learning and by a processing circuit, an action-related corrective policy that once applied reduces a gap associated with an initial simulation state transition function and with a real world state transition function;
- and
- determining a control policy of the robot in a simulator, using the action-related corrective policy.
A non-transitory computer readable medium for learning a robot control policy, the non-transitory computer readable medium stores instruction executable by a processing unit for:
- learning using reinforcement learning and by a processing circuit, an action-related corrective policy that once applied reduces a gap associated with an initial simulation state transition function and with a real world state transition function;
- and
- determining a control policy of the robot in a simulator, using the action-related corrective policy.
A method for controlling a robot, the method comprising:
- sensing information by one or more sensors of the robot;
- and
- controlling a movement of the robot, based on the sensed information, by applying a robot control policy learnt using an action-related corrective policy that once applied reduces a gap associated with an initial simulation state transition function and with a real world state transition function.
A non-transitory computer readable medium for controlling a robot, the non-transitory computer readable medium stores instruction executable by a processing unit for:
- sensing information by one or more sensors of the robot;
- and
- controlling a movement of the robot, based on the sensed information, by applying a robot control policy learnt using an action-related corrective policy that once applied reduces a gap associated with an initial simulation state transition function and with a real world state transition function.
Dependencies are inferred from the claim text.