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Imitation learning works™ – but you need good data 🥹 How to get high-quality visuotactile demos from a bimanual robot with multifingered hands, and learn smooth policies? Check our new work “Learning Visuotactile Skills with Two Multifingered Hands”! 🙌
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We tackle two key challenges: 1. Lack of affordable and accessible teleoperation systems for bimanual multifingered hands 2. Lack of good hand hardware equipped with touch sensing

To address the first challenge, we develop HATO ("dove" 🕊️ in Japanese), a low-cost Hands-Arms TeleOperation system using Meta Quest 2. Our system demonstrates precise motion control capabilities and human-like dexterity -- we even controlled our robot to play Hollow Knight!

To tackle the latter challenge, we adapt two @PSYONICinc prosthetic hands with touch sensors for research, enabling visuotactile data collection with HATO. We learn skills that can complete long-horizon, high-precision tasks and generalize to different environment settings.

We empirically investigate the effects of dataset size, sensing modality, and visual input preprocessing on policy learning. Results and analysis can be found in our paper:

We have also released a comprehensive software suite that supports efficient data collection, multimodal data processing, scalable policy learning, and smooth policy deployment. See our Github repo for more details:

Project page: Huge thanks to amazing collaborators -- @YZ_Franklin, @qiyang_li , @HaozhiQ , @brenthyi, @svlevine, and @JitendraMalikCV !!! from @berkeley_ai 🤖

There have been impressive recent results using tele-op trajectories for training robot policies. These are typically for hands that are parallel jaw grippers; work led by @ToruO_O in our lab at BAIR shows that one can do this now for multifingered hands with vision and touch.

Great work! Congrats

Wow, the Hollow Knight demo is superb! がんばって

awesome 🤩
