X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets
[Uczenie maszynowe] Autorzy: Prithwish Dan, Chenyang Ma, Wei Zhan
Reinforcement learning (RL) in simulation can train dexterous manipulation policies without robot demonstrations, but training a single generalist policy with task-agnostic rewards faces a severe exploration problem: approaching, grasping, and reorienting diverse objects with many degrees of freedom is difficult to discover from scratch. Prior works make exploration tractable with high-quality robot demonstrations, per-task reward shaping, or by restricting policies to narrow modes of behavior. We propose X-Reset, a framework that instead resolves exploration with human hand-object demonstrations. Rather than imitating or tracking retargeted human motion, X-Reset kinematically retargets hand-object states to noisy robot states, filters out states that are unstable in simulation, and samples the remainder as resets during RL training with general-purpose object-centric rewards. The resul…
- Kategoria arxiv
- cs.LG
- Kategoria
- Uczenie maszynowe