IDEAL: Interaction Dynamics and Force-Aware Learning for Dual-Humanoid Collaborative Manipulation

anoid collaborative manipulation extends the payload and workspace capabilities of a single robot, enabling applications such as large-object carrying and cooperative transportation. However, imitation learning for Dual-Humanoid-Object Interaction (DHOI) remains underexplored, particularly in real-world settings where policies must coordinate motion and maintain force consistency under closed-chain coupling and restricted observations. We propose IDEAL, an imitation learning framework for DHOI that combines interaction-consistent kinematic references with explicit dynamics priors. IDEAL first recovers dual-human motions from monocular videos and refines them for interaction consistency. It then formulates collaborative manipulation as a rigid-body grasping problem and solves for desired force allocations under dynamics, contact, and friction constraints. Finally, IDEAL learns control policies through an interaction-dynamics and force-aware reinforcement learning framework guided by both kinematic and dynamic references. Experimental results show that IDEAL successfully achieves robust DHOI in both simulation and real-world deployments. As far as we know, IDEAL is the first end-to-end learning framework for DHOI deployed on real dual-humanoid robots, offering a new paradigm for complex real-world humanoid collaboration.