TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size
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.
69b374c22ad6c5d4cf486a22
Stefan Lionar
claimed_verified
2026-03-13T08:12:56.243Z
63bfb9a54ed1b8fd6883524c
/avatars/5462264a8cf58cb15fcc5194336d45ac.svg
Stefan Lionar
slionar
user
slionar
69b374c22ad6c5d4cf486a23
Gim Hee Lee
TeamHOI: Learning a Unified Policy for Cooperative Human-Object Interactions with Any Team Size
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.
TeamHOI enables decentralized cooperative human-object interaction using a Transformer-based policy with teammate tokens and a masked adversarial motion prior for realistic multi-agent coordination.
Transformer-based policy network teammate tokens masked Adversarial Motion Prior cooperative human-object interaction decentralized policy multi-agent coordination formation reward