TacCoRL: Integrating Tactile Feedback into VLA via Simulation
Vision-language-action (VLA) models provide strong visual, language, and action priors for robot manipulation, but visual observations alone often miss the local contact state required for contact-rich tasks. We present TacCoRL, a scalable framework that injects Tactile feedback into VLA policies and improves them through sim-real Co-training and simulation-based reinforcement learning (RL), without requiring large-scale tactile pretraining or extensive real-world contact exploration. The key idea is not only adding touch as an input, but learning how contact readings should modulate action responses in near-failure states that are rare in demonstrations and risky to collect on hardware. ...
| Authors | Siyu Ma, Yuqi Liang, Chang Yu et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 June 2026 |
| Last updated at source | 10 June 2026 |
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