Active Embodiment Identification with Reinforcement Learning for Legged Robots

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. --- Source: arXiv:2605.08020v1 - http://arxiv.org/abs/2605.08020v1 PDF: https://arxiv.org/pdf/2605.08020v1 Original Link: http://arxiv.org/abs/2605.0...