Unpacking the Individual Components of Diffusion Policy

TL;DR

This paper dissects the Diffusion Policy for robotic imitation learning, analyzing five key components through experiments on benchmarks to understand their individual contributions to performance.

Contribution

It provides a detailed analysis of the five core components of Diffusion Policy, highlighting their roles and importance in robotic imitation learning.

Findings

Observation sequence input significantly impacts performance.

Receding horizon affects adaptability and success.

U-Net and Transformer architectures contribute differently to results.

Abstract

Imitation Learning presents a promising approach for learning generalizable and complex robotic skills. The recently proposed Diffusion Policy generates robot action sequences through a conditional denoising diffusion process, achieving state-of-the-art performance compared to other imitation learning methods. This paper summarizes five key components of Diffusion Policy: 1) observation sequence input; 2) action sequence execution; 3) receding horizon; 4) U-Net or Transformer network architecture; and 5) FiLM conditioning. By conducting experiments across ManiSkill and Adroit benchmarks, this study aims to elucidate the contribution of each component to the success of Diffusion Policy in various scenarios. We hope our findings will provide valuable insights for the application of Diffusion Policy in future research and industry.