TeamHOI：面向任意团队规模协作性人物-物体交互的统一策略学习

TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size

March 9, 2026

作者: Stefan Lionar, Gim Hee Lee

cs.AI

摘要

基于物理的人形机器人控制已在实现逼真高效的单智能体行为方面取得显著进展，但将这些能力扩展到协作式人物交互（HOI）领域仍具挑战性。我们提出TeamHOI框架，通过单一去中心化策略处理任意数量协作智能体间的协同HOI任务。每个智能体基于局部观测进行操作，同时通过基于Transformer策略网络中的队友令牌关注其他成员，实现可变团队规模的可扩展协调。为在缺乏协作HOI数据的情况下保证运动真实性，我们进一步提出掩码对抗运动先验（AMP）策略，该策略在训练时使用单人类参考运动并掩码物体交互的身体部位。掩码区域通过任务奖励引导生成多样且物理合理的协作行为。我们在涉及2至8个人形智能体及不同几何形状物体的协作搬运任务上评估TeamHOI。最后，为促进稳定搬运，我们设计了与团队规模和形状无关的队形奖励机制。TeamHOI以单一策略实现了高成功率，并在多种配置下展现出高度一致的协作能力。

English

Physics-based humanoid control has achieved remarkable progress in enabling realistic and high-performing single-agent behaviors, yet extending these capabilities to cooperative human-object interaction (HOI) remains challenging. We present TeamHOI, a framework that enables a single decentralized policy to handle cooperative HOIs across any number of cooperating agents. Each agent operates using local observations while attending to other teammates through a Transformer-based policy network with teammate tokens, allowing scalable coordination across variable team sizes. To enforce motion realism while addressing the scarcity of cooperative HOI data, we further introduce a masked Adversarial Motion Prior (AMP) strategy that uses single-human reference motions while masking object-interacting body parts during training. The masked regions are then guided through task rewards to produce diverse and physically plausible cooperative behaviors. We evaluate TeamHOI on a challenging cooperative carrying task involving two to eight humanoid agents and varied object geometries. Finally, to promote stable carrying, we design a team-size- and shape-agnostic formation reward. TeamHOI achieves high success rates and demonstrates coherent cooperation across diverse configurations with a single policy.