Article

HA-DiPO: A Diffusion Policy Optimization Framework for Heterogeneous Robot Cooperative Transport

The authors propose HA-DiPO, a hierarchical multi-agent reinforcement learning framework operating under centralized training with decentralized execution (CTDE) for heterogeneous legged robots cooperatively transporting an unfixed shared payload. At the high level, a centralized critic jointly trains each robot's independent coordination policy, and diffusion policies represent multimodal coordination responses; at the low level, embodiment-specific locomotion policies track the coordination commands. The policies are learned from scratch through reward-driven simulation interaction, and at execution they use only local observations with no inter-robot communication.

Part of the event: HA-DiPO Heterogeneous Robot Collaborative Transport Framework Released

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