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

• URL: http://arxiv.org/abs/2603.07988v1
• Date: Mon, 09 Mar 2026 05:52:13 GMT
• Title: TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size
• Abstract summary: Physics-based humanoid control has achieved remarkable progress in enabling realistic and high-performing single-agent behaviors.<n>We present TeamHOI, a framework that enables a single decentralized policy to handle cooperative HOIs across any number of cooperating agents.
• Score: 54.0714652192002
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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