$TF^2$-Steering: Action Refinement with Tactile Forecasts and Feedback via Velocity-Field Steering
Overview
This research introduces $TF^2$-Steering, a method that refines robot actions using tactile forecasts and feedback through velocity-field steering. By employing a tactile mixture-of-experts architecture, the approach corrects and redirects action generation based on tactile signals while leveraging vision-based foundation policies. Experiments across twelve tasks (six simulated, six real-world) show it outperforms baselines by 10.7 percentage points in simulation success rate and 9.1 percentage points in real-world progress success rate.
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- arXiv Robotics — research abstractsSignal$TF^2$-Steering: Action Refinement with Tactile Forecasts and Feedback via Velocity-Field Steering
This research introduces $TF^2$-Steering, a method that refines robot actions using tactile forecasts and feedback through velocity-field steering. By employing a tactile mixture-of-experts architecture, the approach corrects and redirects action generation based on tactile signals while leveraging vision-based foundation policies. Experiments across twelve tasks (six simulated, six real-world) show it outperforms baselines by 10.7 percentage points in simulation success rate and 9.1 percentage points in real-world progress success rate.
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