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For years, I’ve been tuning parameters for robot designs and controllers on specific tasks. Now we can automate this on dataset-scale. Introducing Co-Design of Soft Gripper with Neural Physics - a soft gripper trained in simulation to deform while handling load.

yisha

1,215 subscribers

36,023 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 10

Фото профиля yisha

yisha1 год назад

We derived a uniform pressure model for tendon-driven soft fingers. Instead of using only the fingertips, it utilizes the entire finger by deforming to any shape.

Фото профиля yisha

yisha1 год назад

webpage: paper:

Фото профиля yisha

yisha1 год назад

This is done with my amazing collaborators @baicrystal25, Adabhav Singh, @jianglong_ye , Mike Tolley, @xiaolonw

Фото профиля yisha

yisha1 год назад

✨We show that jointly optimizing on all data performs better than individually optimized grippers✨

Фото профиля Tony Tao

Tony Tao1 год назад

Cool work Yi Sha!

Фото профиля yisha

yisha1 год назад

Thank you ☺️

Фото профиля Sarvesh Patil

Sarvesh Patil1 год назад

This is really cool Yisha!!! Did you find warp easy to use?

Фото профиля yisha

yisha1 год назад

It’s easier to debug because it’s open source. In general I feel like GPU+soft body simulation still has a long way to go 😂

Фото профиля Sarvesh Patil

Sarvesh Patil1 год назад

Haha makes sense! Cheers of the paper!

Фото профиля yisha

yisha1 год назад

We sample diverse design (stiffness) parameters and poses in simulation. We use this to train a neural network surrogate, and then optimize the best design and pose. And then transfer to real robots using structure stiffness in 3d printing.

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20,458 просмотров • 4 месяцев назад