学习协助：通过多智能体强化学习实现物理基础的人机控制
Learning to Assist: Physics-Grounded Human-Human Control via Multi-Agent Reinforcement Learning
AI总结：
本文提出通过多智能体强化学习实现人与人之间力交换的交互动作模仿，解决了连续关注人类伙伴和快速适应其动态的需求，展示了多智能体RL在物理基础和社交感知人形控制中的优势。
摘要：
人形机器人有潜力改变日常服务和照护应用。尽管最近在物理引擎中的通用运动跟踪（GMT）进展使虚拟角色和人形机器人能够重现广泛的人类动作，但这些行为主要局限于无接触的社会互动或孤立动作。助人场景则需要持续关注人类伙伴并快速适应其不断变化的姿势和动态。本文将模仿紧密互动、力交换的人与人动作序列视为多智能体强化学习问题。我们联合训练支持者（助手）代理和接收者代理的伙伴感知策略，以在物理模拟器中跟踪助人动作参考。为使该问题可处理，我们引入伙伴策略初始化方案，将单人运动跟踪控制器的先验知识转移过来，显著提升探索能力。我们进一步提出动态参考重新定位和促进接触的奖励，使助手的参考动作适应接收者的实时姿态，并鼓励物理上有意义的支持。我们证明AssistMimic是首个成功在已建立基准上跟踪助人交互动作的方法，展示了多智能体RL在物理基础和社交感知人形控制中的优势。
英文摘要：
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.
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