Diffusion Transformer Policy

Recent large visual-language action models pretrained on diverse robot datasets have demonstrated the potential for generalizing to new environments with a few in-domain data. However, those approaches usually predict discretized or continuous actions by a small action head, which limits the ability in handling diverse action spaces. In contrast, we model the continuous action with a large multi-modal diffusion transformer, dubbed as Diffusion Transformer Policy, in which we directly denoise action chunks by a large transformer model rather than a small action head. By leveraging the scaling capability of transformers, the proposed approach can effectively model continuous end-effector actions across large diverse robot datasets, and achieve better generalization performance. Extensive experiments demonstrate Diffusion Transformer Policy pretrained on diverse robot data can generalize to different embodiments, including simulation environments like Maniskill2 and Calvin, as well as the real-world Franka arm. Specifically, without bells and whistles, the proposed approach achieves state-ofthe-art performance with only a single third-view camera stream in the Calvin novel task setting (ABC D), improving the average number of tasks completed in a row of 5 to 3.6, and the pretraining stage significantly facilitates the success sequence length on the Calvin by over 1.2. The code will be publicly available. Traditional robot learning paradigm usually relies on large-scale data collected for a specific robot and task, but collecting robot data for generalist tasks is time-consuming and expensive due to the limitations of robot hardware in the real world. Nowadays, the foundational models OpenAI (2022; 2023; 2021); Rombach et al. (2021) in Natural Language Process and Computer Vision, pretrained on broad, diverse, task-agnostic datasets, have demonstrated powerful ability in solving downstream tasks either zero-shot or with a few task-specific samples. It is principally possible that a general robot policy exposed to large scale diverse robot datasets improves generalization and performance on downstream tasks Brohan et al. (2022; 2023).