MTDP: A Modulated Transformer based Diffusion Policy Model

TL;DR

This paper introduces the MTDP model, a novel Modulated Transformer architecture with a specialized attention mechanism, significantly improving robot manipulation success rates using diffusion policies.

Contribution

It proposes the Modulated Transformer Diffusion Policy (MTDP) with a new Modulated Attention module, enhancing integration of guiding conditions and boosting manipulation task performance.

Findings

MTDP outperforms existing Transformer architectures in manipulation tasks.

Success rate increased by 12% in the Toolhang experiment.

Applying Modulated Attention to UNet architecture improved success rates across experiments.

Abstract

Recent research on robot manipulation based on Behavior Cloning (BC) has made significant progress. By combining diffusion models with BC, diffusion policiy has been proposed, enabling robots to quickly learn manipulation tasks with high success rates. However, integrating diffusion policy with high-capacity Transformer presents challenges, traditional Transformer architectures struggle to effectively integrate guiding conditions, resulting in poor performance in manipulation tasks when using Transformer-based models. In this paper, we investigate key architectural designs of Transformers and improve the traditional Transformer architecture by proposing the Modulated Transformer Diffusion Policy (MTDP) model for diffusion policy. The core of this model is the Modulated Attention module we proposed, which more effectively integrates the guiding conditions with the main input, improving the generative model's output quality and, consequently, increasing the robot's task success rate. In six experimental tasks, MTDP outperformed existing Transformer model architectures, particularly in the Toolhang experiment, where the success rate increased by 12\%. To verify the generality of Modulated Attention, we applied it to the UNet architecture to construct Modulated UNet Diffusion Policy model (MUDP), which also achieved higher success rates than existing UNet architectures across all six experiments. The Diffusion Policy uses Denoising Diffusion Probabilistic Models (DDPM) as the diffusion model. Building on this, we also explored Denoising Diffusion Implicit Models (DDIM) as the diffusion model, constructing the MTDP-I and MUDP-I model, which nearly doubled the generation speed while maintaining performance.