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Excited to share my final PhD project😀 We show how simple, yet elegant changes enable diffusion transformers to learn SOTA robotic policies on real robots. Our method improves performance by 20% across a wide range of highly dexterous tasks - like cutting sushi! 1/n

Sudeep Dasari

1,482 subscribers

20,536 次观看 • 1 年前 •via X (Twitter)

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Sudeep Dasari 的头像

Sudeep Dasari1 年前

Our method, DiT-Block Policy, works by adding AdaLN layers to the decoder of a standard transformer diffusion policy. This significantly outperformed standard cross-attention blocks, especially when using fewer DDIM iterations during inference. 2/n

Sudeep Dasari 的头像

Sudeep Dasari1 年前

We release all data and code from our project. This includes BiPlay - a more diverse bi-manual manipulation dataset. Each episode in BiPlay consists of randomized objects, tasks, and settings with accompanied language annotations for scalable learning. 3/n

Sudeep Dasari 的头像

Sudeep Dasari1 年前

Finally, I’d like to give a shoutout to my collaborators @oier_mees, Sebastian Zhao, @mohansrirama, and @svlevine who made this project possible! For more information, check out our website: n/n

Sabeer Saeed 的头像

Sabeer Saeed1 年前

Superb Work!

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