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ForgetMimic: Motion Unlearning for Reinforcement Learning Humanoid Control

Paper recorded by Signals 4 on 2026-09-23 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-23 on arXiv · recorded by Signals 4 on 2026-09-24

Category: cs.LG · 机器学习 · first seen 2026-09-24

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:

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#12 most recent of 278 cs.LG papers we have recorded · ↑ newer: Quantum score matching with applications to learning thermal states · ↓ older: LEAP-CBF: A Safety Filter for Uncertain Systems with Least-Effort Adve
Cite this page: ForgetMimic: Motion Unlearning for Reinforcement Learning Humanoid Control: the #12 most recent of 278 cs.LG papers we have recorded (as of 2026-09-23). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/forgetmimic-motion-unlearning-for-reinforcement-learning-humanoid-control.html
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