Haode Zhang

MS @CarnegieMellon | Previous BS @sjtu1896

[Haode Zhang @HaodeZ47056]

Super excited that our paper won the Best Paper Award! 🎉
Grateful to be part of this work and lucky to collaborate with such an awesome team. More to come 🚀

[Kelin Yu @ColinYu14116982]

OmniTacTune won the Best Paper Award at the Tactile Sensing for Robotic Foundation Models Workshop at RSS 2026! (https://t.co/N3pNabWg5K)

Huge thanks to the coauthors @HaodeZ47056 and @HanYunhai, and our advisors @h_ravichandar and @RuohanGao1.

[Haode Zhang @HaodeZ47056]

@ColinYu14116982 @HanYunhai @h_ravichandar @RuohanGao1 Couldn't have done it without the awesome team 😊 Looking forward to more collaborations!

[Kelin Yu @ColinYu14116982]

1/ 👀Vision tells robots where to go. 👋Touch tells robots about the interactions.
2/ Visual policies from teleoperated robot demos and human videos are scaling fast — but they don't have paired tactile data, so they still fail at the last millimeter of contact-rich manipulation.

❓So here is the question:
How can we adapt tactile feedback into pretrained visual policies?

Humans solve this naturally.
1️⃣ Learn from demonstrations through vision: we understand the task structure and the motion priors.
2️⃣ Then practice with touch: we interact with the world, feel what happens, and refine the motion.

🚀 We introduce OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies ⬇️
✦ Adapting tactile feedback into existing visual policies
✦ No offline tactile demonstrations
✦ Real-world RL in 40–80 minutes, 5–40% → 85–100%
✦ Works across human flow policies, ACT, DP, π0.5
✦ Works across different tactile representations

🌐 Website: https://t.co/I6GOpdUrhP
📖 Paper: https://t.co/YzKRucxxnu
📷 Video: https://t.co/dryarXliCK

In this demo, we show four challenging contact-rich manipulation tasks and a 40-mins, one-take recording of an online RL training demo.

1/n

[Haode Zhang @HaodeZ47056]

@Jiarui_X @GalaxeaDynamics wonderful😍