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It is really hard to get robot policies that are both precise (small margins for error) and general (robust to env variations). We just released ViSk, where skin sensing is used to train fine-grained policies with ~1 hour of data. Below is a single-take video.

Lerrel Pinto

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17,169 次观看 • 1 年前 •via X (Twitter)

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Lerrel Pinto 的头像

Lerrel Pinto1 年前

The key technical idea in ViSk is that simply treating skin-based touch data as tokens for a transformer is enough to get multi-modal (vision+touch) policies. For the transformer architecture, we just use BAKU from @haldar_siddhant .

Lerrel Pinto 的头像

Lerrel Pinto1 年前

An empirical insight is that skin-sensing significantly improves generalization to positions, size, shape, and type of objects. This means that you do not need to collect 1000s of demos. All of our tasks needed <200 demos to train.

Lerrel Pinto 的头像

Lerrel Pinto1 年前

This work was led by @venkyp2000 and @Raunaqmb with @Yifeng_C and @haldar_siddhant . More details (paper, code and videos) are here:

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