# Diffusion Co-Policy for Synergistic Human-Robot Collaborative Tasks

arXiv: https://arxiv.org/abs/2305.12171 · HTML: https://mlsanity.com/paper/2305.12171/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2023.

## Built on by (most cited first, 15 of 15)

- [Diffusion Models for Reinforcement Learning: A Survey](https://mlsanity.com/paper/2311.01223/) (2023)
- [DexDiffuser: Generating Dexterous Grasps with Diffusion Models](https://mlsanity.com/paper/2402.02989/) (2024)
- [CORE4D: A 4D Human-Object-Human Interaction Dataset for Collaborative Object REarrangement](https://mlsanity.com/paper/2406.19353/) (2024)
- [Inference-Time Policy Steering through Human Interactions](https://mlsanity.com/paper/2411.16627/) (2024)
- [PDP: Physics-Based Character Animation via Diffusion Policy](https://mlsanity.com/paper/2406.00960/) (2024)
- [Don't Start from Scratch: Behavioral Refinement via Interpolant-based Policy Diffusion](https://mlsanity.com/paper/2402.16075/) (2024)
- [Diffusion Model for Planning: A Systematic Literature Review](https://mlsanity.com/paper/2408.10266/) (2024)
- [Learning Diffusion Policies from Demonstrations For Compliant Contact-rich Manipulation](https://mlsanity.com/paper/2410.19235/) (2024)
- [Uni-Inter: Unifying 3D Human Motion Synthesis Across Diverse Interaction Contexts](https://mlsanity.com/paper/2511.13032/) (2025)
- [MoVEInt: Mixture of Variational Experts for Learning Human-Robot Interactions from Demonstrations](https://mlsanity.com/paper/2407.07636/) (2024)
- [Autonomous Human-Robot Interaction via Operator Imitation](https://mlsanity.com/paper/2504.02724/) (2025)
- [Robot Motion Planning using One-Step Diffusion with Noise-Optimized Approximate Motions](https://mlsanity.com/paper/2504.19652/) (2025)
- [Learning Multimodal Latent Dynamics for Human-Robot Interaction](https://mlsanity.com/paper/2311.16380/) (2023)
- [Learning from Humans for Proactive Assistance in Human-Robot Collaborative Transport](https://mlsanity.com/paper/2609.25351/) (2026)
- [Watch, Infer, Coordinate: Inferring Robot Partner Constraints for Zero-Shot Coordination](https://mlsanity.com/paper/2610.02170/) (2026)

## Abstract (arXiv)

Modeling multimodal human behavior has been a key barrier to increasing the level of interaction between human and robot, particularly for collaborative tasks. Our key insight is that an effective, learned robot policy used for human-robot collaborative tasks must be able to express a high degree of multimodality, predict actions in a temporally consistent manner, and recognize a wide range of frequencies of human actions in order to seamlessly integrate with a human in the control loop. We present Diffusion Co-policy, a method for planning sequences of actions that synergize well with humans during test time. The co-policy predicts joint human-robot action sequences via a Transformer-based diffusion model, which is trained on a dataset of collaborative human-human demonstrations, and directly executes the robot actions in a receding horizon control framework. We demonstrate in both simulation and real environments that the method outperforms other state-of-art learning methods on the task of human-robot table-carrying with a human in the loop. Moreover, we qualitatively highlight compelling robot behaviors that demonstrate evidence of true human-robot collaboration, including mutual adaptation, shared task understanding, leadership switching, and low levels of wasteful interaction forces arising from dissent.