ForceRFT: Refining VLA Actions through Force-Guided Residual Reinforcement Learning
cs.RO
Submitted: 2026-09-19
Updated: 2026-09-19
Terminology
Sources
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
- From Foundation to Application: Improving VLA Models in Practice
- G0.5: One Autoregressive Stream for Robot Reasoning and Action
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- OpenVLA: An Open-Source Vision-Language-Action Model
- ForceFlow: Learning to Feel and Act via Contact-Driven Flow Matching
- FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulation
- FM-VLA: Force-based Memory for Vision-Language-Action Models in Contact-Rich Manipulation
- Facet-0: A Robotic Foundation Model for Contact-Rich Precise Manipulation
- FAWAM: Force-Aware World Action Models for Closed-Loop Contact-Rich Manipulation
- FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation
- ForceVLA2: Unleashing Hybrid Force-Position Control with Force Awareness for Contact-Rich Manipulation
- CompliantVLA-adaptor: VLM-Guided Variable Impedance Action for Safe Contact-Rich Manipulation
- Human-in-the-Loop Imitation Learning using Remote Teleoperation
- CRAFT: Adapting VLA Models to Contact-rich Manipulation via Force-aware Curriculum Fine-tuning
- From Imitation to Refinement -- Residual RL for Precise Assembly
- Residual Off-Policy RL for Finetuning Behavior Cloning Policies
- Residual Reinforcement Learning for Robot Control
- TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation
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