Article

OpenDoge uses observation-history implicit state estimation to control quadruped robot locomotion

OpenDoge proposes a hybrid internal model for quadruped robot locomotion control that, without terrain maps or contact sensors, encodes observation history into a velocity estimate and a 16-dimensional latent state to determine gait phase and external disturbances. The article combines real inference data to examine what information the latent state carries and what it lacks.

Part of the event: OpenDoge controls quadruped robot without maps or contact sensors

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