PEARS: Physical-Prior-Guided Efficient Adaptation via Failure Reasoning and Diffusion Steering for Tactile Manipulation
cs.RO
Submitted: 2026-10-06
Updated: 2026-10-06
Comments: 9 pages, 4 figures. Project website: https://song-kun.github.io/pears
Project page: https://song-kun.github.io/pears
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation
- Demystifying When and Why VLAs Fail in Contact-Rich Tasks and How to Fix Them
- TacCoRL: Integrating Tactile Feedback into VLA via Simulation
- OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
- UniVTAC: A Unified Simulation Platform for Visuo-Tactile Manipulation Data Generation, Learning, and Benchmarking
- VT-MUSE: Multimodal Unified Sequential Visuotactile Representation Learning for Manipulation
- TaF-VLA: Tactile-Force Alignment in Vision-Language-Action Models for Force-aware Manipulation
- OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation
- T-Rex: Tactile-Reactive Dexterous Manipulation
- FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation
- UniTacVLA: Unified Tactile Understanding and Prediction in Vision Language Action Models
- VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
- RL Token: Bootstrapping Online RL with Vision-Language-Action Models
- TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation
- Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
- Residual Off-Policy RL for Finetuning Behavior Cloning Policies
- Qwen3-VL Technical Report
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