Learning robust legged robot locomotion with implicit terrain imagination via deep reinforcement learning
AssigneeKorea Advanced Institute of Science and Technology
InventorsHyun MYUNG, I Made Aswin NAHRENDRA, Byeongho YU
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Abstract
Disclosed is technology for controlling deep reinforcement learning-based legged robot locomotion by inferring implicit terrain information. A legged robot control method may include inferring an action of a quadrupedal robot from proprioception through a deep reinforcement learning-legged robot model, and a locomotion policy that implicitly infers properties of terrains through which the quadrupedal robot moves may be learned in the legged robot model.
Claims20
A legged robot control method performed by a computer device, wherein the computer device comprises at least one processor configured to execute computer-readable instructions included in a memory,
- the legged robot control method comprises inferring, by the at least one processor, an action of a quadrupedal robot from proprioception through a deep reinforcement learning-legged robot model, and
- a locomotion policy that implicitly infers properties of terrains through which the quadrupedal robot moves is learned in the legged robot model.
A non-transitory computer-readable recording medium storing instructions that, when executed by a processor, cause the processor to perform a legged robot control method comprising inferring an action of a quadrupedal robot from proprioception through a deep reinforcement learning-legged robot model, wherein a locomotion policy that implicitly infers properties of terrains through which the quadrupedal robot moves is learned in the legged robot model.
A computer-implemented legged robot control system comprising:
- at least one processor configured to execute computer-readable instructions included in a memory,
- wherein the at least one processor is configured to process a process of inferring an action of a quadrupedal robot from proprioception through a deep reinforcement learning-legged robot model, and
- a locomotion policy that implicitly infers properties of terrains through which the quadrupedal robot moves is learned in the legged robot model.
Dependencies are inferred from the claim text.