Tactile Adaptive Robot Paper Wins Best Paper
Scobleizer · x · 2026-07-18
OmniTacTune won the Best Paper Award at the Tactile Sensing for Robotic Foundation Models Workshop at RSS 2026.
This work focuses on a specific issue: while visual policies can learn task structures and motion priors from teleoperated robot demonstrations and human videos, the lack of paired tactile data often leads to failures in contact-intensive, last-few-millimeter manipulations. The authors propose using tactile residuals to adapt pre-trained visual policies, enabling the robot to acquire contact-phase capabilities without relying on inherently available tactile data.
More from Embodied
- Tesla expands Robotaxi rides to seven areas, including new Orlando and Tampa zones — elonmusk · 2026-07-22
- Hands-on robotics workshop on Saturday may be the last in-person session before August — StewartalsopIII · 2026-07-22
- NVIDIA pitches World Foundation Models as a way to scale physical AI data generation — MonaJalal_ · 2026-07-22
- RoboMME Podcast Preview: Benchmarking Memory for Robotic Policies — chris_j_paxton · 2026-07-21
- Gritt says an 8-person crew now installs 3,000 to 4,000 solar panels a day — HaktanSuren · 2026-07-21
- A helium-powered flying robot whale aims to be a quiet companion pet — chris_j_paxton · 2026-07-21