Generalists Act, Specialists Intervene: Modular Stage-Selective Reinforcement Learning for Vision-Language-Action Manipulation
cs.RO
Submitted: 2026-09-22
Updated: 2026-10-08
Code: https://github.com/RLinf/RLinf
Terminology
Sources
- WireCraft: A Simulation Benchmark for Industrial DLO Manipulation
- RL Token: Bootstrapping Online RL with Vision-Language-Action Models
- Foresight Residual RL for Long-Horizon Robot Manipulation with Vision-Language-Action Models
- SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics
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