GraspTune: Tactile-Driven Execution Refinement for Robust Grasping
cs.RO
Submitted: 2026-09-21
Updated: 2026-09-21
Terminology
Sources
- Proximal Policy Optimization Algorithms
- Control of the Final-Phase of Closed-Loop Visual Grasping using Image-Based Visual Servoing
- D3Grasp: Diverse and Deformable Dexterous Grasping for General Objects
- TacRefineNet: Goal-Conditioned Tactile Grasp Refinement for Edge-Prominent Objects
- TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance
- OmniTacTune: Policy-Agnostic Real-World RL for Tactile Residual Adaptation of Visual Policies
- TacCoRL: Integrating Tactile Feedback into VLA via Simulation
Related papers
- FMT x: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
- MPCFormer: A physics-informed data-driven approach for explainable socially-aware autonomous driving
- RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies
- Fine-tuning is Not Enough: A Parallel Framework for Collaborative Imitation and Reinforcement Learning in End-to-end Autonomous Driving