4 papers
Safe Reinforcement Learning with Receding-Horizon Safety Critic and Differentiable Action Shields
Siwei Ju, Jan Peters, Oleg Arenz
Despite the Markov assumption, safety failures in robot reinforcement learning rarely occur instantaneously; they typically emerge over several steps as the policy enters unfamilia…
Continue, Abort, or Fall: Viability-Aware Policy Selection (VAPS) for Safe Humanoid Acrobatics
Siwei Ju, Lu Liu, Jan Peters +1
Dynamic humanoid motions such as flips risk hardware damage due to suboptimal policies, disturbances or sim-to-real gaps. A motion tracking policy offers no way out once the maneuv…
CrossBFM: Distilling a Shared Latent Behavior Space Across Humanoid Embodiments
Tan-Dzung Do, Tuan Dat Phuong, Nico Bohlinger +6
Behavior Foundation Models (BFMs) give humanoids a promptable policy over a latent behavior space, enabling one single vector to represent a motion to imitate, a pose to reach, or…
Behavior-Constrained Reinforcement Learning with Receding-Horizon Credit Assignment for High-Performance Control
Siwei Ju, Jan Tauberschmidt, Oleg Arenz +2
Learning high-performance control policies that remain consistent with expert behavior is a fundamental challenge in robotics. Reinforcement learning can discover high-performing s…