Active Embodiment Identification with Reinforcement Learning for Legged Robots

Nico Bohlinger, Jan Peters

Keywords: Embodiment Identification, Reinforcement Learning, Legged Robots

Abstract:

We present an active embodiment identification method for legged robots that jointly learns information-seeking behavior and explicit embodiment prediction.
Using a history-augmented URMA architecture, the method infers joint-level and global embodiment parameters through interaction with the environment in simulation across different morphologies.

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Submission Number: 25