# ForgetMimic：面向强化学习人形机器人控制的运动遗忘

> 原文链接：https://robotics.hellobike.com/papers/2026-09-23-2609-28378
> 频道：papers | 更新日期：2026-09-23 | 标签：arxiv, cs.RO
> 核心摘要：针对人形机器人策略中特定动作难以移除的问题，ForgetMimic以运动级遗忘方法在Unitree G1和H2的12种动作上验证。

# ForgetMimic：面向强化学习人形机器人控制的运动遗忘

::: v-pre
> 原文链接：[ForgetMimic: Motion Unlearning for Reinforcement Learning Humanoid Control](https://arxiv.org/abs/2609.28378)（arXiv 2609.28378 · 发布日期：2026-09-23）

**ForgetMimic: Motion Unlearning for Reinforcement Learning Humanoid Control**

针对人形机器人策略中特定动作难以移除的问题，ForgetMimic以运动级遗忘方法在Unitree G1和H2的12种动作上验证。

## 核心要点

- 人形控制借助人类示范与强化学习已实现多样灵活动作，但特定动作移除仍探索不足。
- 该问题源于安全与隐私需求，包括删除恶意、中毒、次优或受版权保护的动作。
- ForgetMimic降低目标K个动作的表现，同时保持其余N-K个动作的有效性。
- 实验表明其能消除指定动作记忆，并维持其他动作的正常运行。

**验证范围与局限**：验证范围为Unitree G1和H2人形机器人上的12种动作，包括Dance、Fight、Flip等。

## 英文摘要 (Abstract)

Humanoid control, leveraging human demonstrations, has achieved diverse, agile, and natural locomotion behaviors through reinforcement learning (RL). While this paradigm has yielded remarkable performance in physical humanoid control, how to eliminate specific motions from learned policies remains insufficiently explored. Addressing this issue is motivated by pressing safety and privacy concerns: the removal of malicious, poisoned, or suboptimal motions, as well as copyright-protected motions subject to the right to be forgotten under regulations such as the GDPR, is of critical importance. To this end, we propose {ForgetMimic}, the first motion-level unlearning method designed specifically for physical-world humanoid control. The core idea of ForgetMimic is as follows: given a policy $π_θ$ trained on $N$ motions, our method degrades performance on a target subset of $K$ motions while preserving the effectiveness of the remaining $N-K$ motions. Furthermore, we identify and resolve two key training mechanisms in robot control that lead to unlearning failure. We conduct extensive experiments on the Unitree G1 and H2 humanoid robots across 12 motions, including Dance, Fight, Flip, and others. Experimental results demonstrate that ForgetMimic effectively eliminates memory of designated motions while maintaining the normal operation of all other motions.

作者：Xukun Luan, Zhongxiang Lei, Chen Gong, Shaowei Li, Yuanguo Bi, Jinyan Liu
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## 原始参考来源 (Sources)

- [arXiv 2609.28378：ForgetMimic: Motion Unlearning for Reinforcement Learning Humanoid Control](https://arxiv.org/abs/2609.28378)