PF-RL: Progress Field Reinforcement Learning via Goal-Conditioned Value Geometry for Vision-Language-Action Models
cs.RO
Submitted: 2026-09-26
Updated: 2026-09-26
Code: https://github.com/MINT-SJTU/Evo-RL
Terminology
Sources
- EVOLVE-VLA: Test-Time Training from Environment Feedback for Vision-Language-Action Models
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- LaST-R1: Reinforcing Robotic Manipulation via Adaptive Physical Latent Reasoning
- $\pi_\texttt{RL}$: Online RL Fine-tuning for Flow-based Vision-Language-Action Models
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization
- SRPO: Self-Referential Policy Optimization for Vision-Language-Action Models
- Self-Improving Embodied Foundation Models
- NORA-1.5: A Vision-Language-Action Model Trained using World Model- and Action-based Preference Rewards
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
- OpenVLA: An Open-Source Vision-Language-Action Model
- ProgVLA: Progress-Aware Robot Manipulation Skill Learning
- ExToken: Structured Exploration for Efficient Vision-Language-Action Reinforcement Fine-tuning
- Robometer: Scaling General-Purpose Robotic Reward Models via Trajectory Comparisons
- DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization
- RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation
- VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning
- VIP: Towards Universal Visual Reward and Representation via Value-Implicit Pre-Training
- Representation Learning with Contrastive Predictive Coding
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