Active Embodiment Identification with Reinforcement Learning for Legged Robots

Nico Bohlinger; Jan Peters

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arXiv · 2605.08020

Active Embodiment Identification with Reinforcement Learning for Legged Robots

Nico Bohlinger·Jan Peters

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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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Nico Bohlinger, Jan Peters. 2026-05-08. Active Embodiment Identification with Reinforcement Learning for Legged Robots. https://arxiv.org/abs/2605.08020

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