Venue
Robotics: Science and Systems
Domain
robotics, machine learning
This paper introduces Diffusion Policy, a new way of generating robot behavior by representing a robot's visuomotor policy as a conditional denoising diffusion process.We benchmark Diffusion Policy across 15 different tasks from 4 different robot manipulation benchmarks and find that it consistently outperforms existing state-of-the-art robot learning methods with an average improvement of 46.9%.Diffusion Policy learns the gradient of the action-distribution score function and iteratively optimizes with respect to this gradient field during inference via a series of stochastic Langevin dynamics steps.We find that the diffusion formulation yields powerful advantages when used for robot policies, including gracefully handling multimodal action distributions, being suitable for high-dimensional action spaces, and exhibiting impressive training stability.To fully unlock the potential of diffusion models for visuomotor policy learning on physical robots, this paper presents a set of key technical contributions including the incorporation of receding horizon control, visual conditioning, and the time-series diffusion transformer.We hope this work will help motivate a new generation of policy learning techniques that are able to leverage the powerful generative modeling capabilities of diffusion models.Code, data, and training details is available diffusion-policy.cs.columbia.edu
This paper presents Diffusion Policy, a novel framework for generating robot behavior through a conditional denoising diffusion process. It benchmarks the model across 15 tasks from 4 robot manipulation benchmarks, demonstrating a significant performance improvement of 46.9% over existing state-of-the-art methods. The Diffusion Policy leverages stochastic Langevin dynamics for optimizing the action-distribution gradient, allowing it to handle multimodal action distributions effectively, operate in high-dimensional action spaces, and maintain training stability. Key contributions include integrating receding horizon control, visual conditioning, and a new time-series diffusion transformer architecture for improved action prediction. The paper emphasizes the effectiveness of diffusion models for visuomotor policy learning and provides extensive experimental evaluation, including both simulated and real-world tasks, revealing promising results in complex manipulation scenarios.
This paper employs the following methods:
- Action Diffusion
- Stochastic Langevin Dynamics
- Receding Horizon Control
- Time-Series Diffusion Transformer
The following datasets were used in this research:
- Outperforms existing methods with an average improvement of 46.9%
- Demonstrated effectiveness across 15 tasks from 4 benchmarks
The authors identified the following limitations:
- Number of GPUs: 1
- GPU Type: Nvidia 3080
diffusion policy
visuomotor policy
robot manipulation
behavior cloning
generative models