Wenjie Jeff Li

PhD @UCBerkeley | @berkeley_ai | Robot learning

[Binghao Huang @binghao_huang]

An elegant way to add shear sensitivity to FlexiTac! TacGB uses passive bumps to encode shear into pressure patterns that robot policies can learn from directly, without changing the electronics.

Great to see @jeffwli and team build on our open-source hardware!

[Wenjie Jeff Li @jeffwli]

@binghao_huang Thanks, @binghao_huang ! Really appreciate you and the team making FlexiTac open source 🙌

It's been exciting to build on your hardware and explore how a simple layer of bumps can make pressure maps more informative for robot learning.

[Wenjie Jeff Li @jeffwli]

@carrieeeeet Amazing platform!

[Lezhi (Carrie) Tan @carrieeeeet]

Superintelligence should benefit human intelligence. Mathematics is our starting point.

We’re launching Readable Intelligence, and we invite the math and AI communities to build it with us.
📄 Pick a paper.
💬 Flag an unclear step.
🙌 Contribute a more readable version....
https://t.co/IM5pH7oM2g

[Wenjie Jeff Li @jeffwli]

Update: the human demonstration data used for training in TacGB is now available on Hugging Face!

🤗 Dataset: https://t.co/N4Kq249HCM

We hope this is useful for others working on tactile robot learning. Questions and feedback are welcome!

[Wenjie Jeff Li @jeffwli]

We gave tactile sensors goosebumps and helped robots feel friction. 🤖🦾
👉 Using ordinary cabinet bumpers.

Meet TacGooseBumps (TacGB), a passive add-on that sticks directly onto existing normal-only tactile sensors, with no changes to their electronics.

How does it work? Under tangential loads, the bumps tilt and redistribute pressure, creating patterns that a robot policy can learn from directly, with no explicit force reconstruction.

Across four manipulation tasks, TacGB improves insertion success by up to 36 percentage points, enables gentler egg placement, and produces more continuous, straighter drawing.

🌐 Project: https://t.co/vumTiYlAQw
📄 Paper: https://t.co/xIfpRG3uYi