外观
论文arXiv 2609.357151 个月前
X-Reset：通过跨具身重置扩展以物体为中心的强化学习
原文链接：X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets（arXiv 2609.35715 · 发布日期：2026-09-28）
X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets
X-Reset用人手-物体演示生成RL重置，缓解通用灵巧操作探索难题，并在20个物体、三种具身及Sim-to-Real上验证。
核心要点
- 仿真RL可免机器人演示训练灵巧操作，但通用策略在任务无关奖励下从零探索很困难。
- 方法将人手-物体状态运动学重定向为带噪机器人状态，过滤仿真中不稳定状态后作为RL重置。
- 所得策略仅依赖物体状态和目标，演示只通过重置分布进入训练，而非模仿或跟踪重定向动作。
- 实验在20个物体和三种具身上训练通用策略，并显示可扩展到更多训练物体、泛化到未见物体、零样本Sim-to-Real迁移。
验证范围与局限：验证范围为20个物体、三种具身的仿真训练，并包含零样本Sim-to-Real迁移。
英文摘要 (Abstract)
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 resulting policy depends only on object state and goal, with demonstrations entering training through the reset distribution. We show that X-Reset trains generalist policies on 20 objects across three embodiments---a 22-DoF hand on two different arms and a parallel-jaw gripper---and resolves the exploration challenges of RL from scratch. X-Reset scales with the number of training objects, generalizes to unseen objects, can learn from imperfect hand-pose estimates, and transfers behaviors zero-shot from sim-to-real.
作者：Prithwish Dan, Chenyang Ma, Wei Zhan
来源
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