Humanoid robotics has strong potential to transform daily service and caregiving applications. Although recent advances in general motion tracking within physics engines (GMT) have enabled virtual characters and humanoid robots to reproduce a broad range of human motions, these behaviors are primarily limited to contact-less social interactions or isolated movements. Assistive scenarios, by contrast, require continuous awareness of a human partner and rapid adaptation to their evolving posture and dynamics. In this paper, we formulate the imitation of closely interacting, force-exchanging human-human motion sequences as a multi-agent reinforcement learning problem. We jointly train partner-aware policies for both the supporter (assistant) agent and the recipient agent in a physics simulator to track assistive motion references. To make this problem tractable, we introduce a partner policies initialization scheme that transfers priors from single-human motion-tracking controllers, greatly improving exploration. We further propose dynamic reference retargeting and contact-promoting reward, which adapt the assistant's reference motion to the recipient's real-time pose and encourage physically meaningful support. We show that AssistMimic is the first method capable of successfully tracking assistive interaction motions on established benchmarks, demonstrating the benefits of a multi-agent RL formulation for physically grounded and socially aware humanoid control.

翻译：人形机器人有潜力彻底改变日常服务与护理应用。尽管物理引擎中通用运动追踪（GMT）的最新进展已使虚拟角色和人形机器人能够复现多种人类运动，但这些行为主要局限于无接触的社交互动或孤立动作。相比之下，辅助场景要求持续感知人类伙伴并快速适应其变化的姿态与动力学特性。本文将以紧密交互、存在力传递的人-人运动序列模仿问题建模为多智能体强化学习任务。我们在物理仿真器中联合训练支持者（辅助）智能体和接收者智能体的伙伴感知策略，以跟踪辅助运动参考。为解决该问题的可解性，我们提出一种伙伴策略初始化方案，通过迁移单人运动跟踪控制器的先验知识大幅提升探索效率。进一步，我们提出动态参考重定向与接触激励奖励机制，使辅助者的参考运动自适应接收者的实时姿态，并促进物理有意义的支撑行为。实验表明，AssistMimic是首个能够在既有基准上成功跟踪辅助交互运动的方法，论证了多智能体强化学习框架在实现物理可信与社会感知的人形控制中的优势。