BEE: Intervention-Adaptive Real-World Reinforcement Learning with Vision-Language-Action Models
cs.RO, cs.AI
Submitted: 2026-09-23
Updated: 2026-09-23
Terminology
Sources
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning
- Steering Your Diffusion Policy with Latent Space Reinforcement Learning
- RL Token: Bootstrapping Online RL with Vision-Language-Action Models
- Real-world Reinforcement Learning from Suboptimal Interventions
- VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
- Interactive Post-Training for Vision-Language-Action Models
- GRAPE: Generalizing Robot Policy via Preference Alignment
- RLDG: Robotic Generalist Policy Distillation via Reinforcement Learning
- AWAC: Accelerating Online Reinforcement Learning with Offline Datasets
- Residual Policy Learning
- LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization
- LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
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