CoWBC: Learning Whole-Body Control for unconstrained Cooperative transport with Humanoid Robots

Unconstrained cooperative transport requires humanoid robots to coordinate whole-body actions while maintaining contact with and stable support for a shared object. However, physical coupling through the shared object makes stable whole-body control more challenging than in single-humanoid settings. To address this challenge, we introduce CoWBC, an object-centered framework that learns role-aware cooperative transport policies from real-world human collaboration demonstrations, where roles describe the robots’ complementary contributions to the shared transport task. CoWBC uses a shared base policy to learn common cooperative skills and role-specific residuals to adapt actions to each robot’s role. Specifically, we first reconstructs interactions between human collaborators and a shared object from synchronized multi-view videos and retargets them into coordinated robot reference motions, preserving the collaborators’ relative positions, end-effector–object geometry, and temporal alignment. We then progressively train a centralized teacher with cooperation-aware rewards, then distill its behavior into local student policies. To mitigate the state distribution shift induced by closed-loop student execution, we further augment distillation with lightweight learner-state refinement, where the centralized teacher provides additional supervision on states visited by the current student. This refinement improves closed-loop execution in our evaluations. Extensive experiments show that our dataset achieves a mean valid wrist contact rate 41.10 percentage points higher than that of the evaluated CORE4D subset. CoWBC achieves a safe success rate of 80.18%. Our https://iclr-visualization.pages.dev/project website showcases the workflow and visualization results, making them accessible to the community for future research on humanoid cooperation.