# 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：https://robotics.hellobike.com/papers/2026-09-28-2609-35715
> 频道：papers | 更新日期：2026-09-28 | 标签：arxiv, cs.RO
> 核心摘要：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。

# X-Reset：通过跨具身重置扩展以物体为中心的强化学习

::: v-pre
> 原文链接：[X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets](https://arxiv.org/abs/2609.35715)（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
:::

## 原始参考来源 (Sources)

- [arXiv 2609.35715：X-Reset: Scaling Object-Centric Reinforcement Learning via Cross-Embodiment Resets](https://arxiv.org/abs/2609.35715)