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

- Source: arXiv cs.RO
- Published: 2026-10-27T04:00:00.000Z
- RoboHot: https://robohot.news/en/items/sj6ftxdnvu1lrzaj3av3qu5qm
- Original: https://arxiv.org/abs/2610.33584

## Summary

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.