DEFT-VLA: Stage-Localized Force Specialization for Contact-Rich Robotic Manipulation
cs.RO
Submitted: 2026-10-10
Updated: 2026-10-10
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Sources
- CRAFT: Adapting VLA Models to Contact-rich Manipulation via Force-aware Curriculum Fine-tuning
- Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization
- TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation
- FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation
- FACTR 2: Learning External Force Sensing for Commodity Robot Arms Improves Policy Learning
- Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection
- PriorVLA: Prior-Preserving Adaptation for Vision-Language-Action Models
- Knowledge Insulating Vision-Language-Action Models: Train Fast, Run Fast, Generalize Better
- FM-VLA: Force-based Memory for Vision-Language-Action Models in Contact-Rich Manipulation
- FWBC-VLA: Force-Aware Whole-Body Compensation for Contact-Rich Loco-Manipulation
- Force-Aware Residual DAgger via Trajectory Editing for Precision Insertion with Impedance Control
- TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation
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
- ARC: A Reasoning Recipe for Robot Foundation Models
- HRDexDB: A 4D Dexterous Grasping Dataset Across Human and Multiple Robot Embodiments
- APT: Action Expert Pretraining Improves Instruction Generalization of Vision-Language-Action Policies