ProcVLM: Learning Procedure-Grounded Progress Rewards for Robotic Manipulation
cs.RO, cs.LG
Submitted: 2026-05-09
Updated: 2026-09-26
Code: https://github.com/MINT-SJTU/Evo-RL
Project page: https://procvlm.github.io
Terminology
Sources
- $\pi^{*}_{0.6}$: a VLA That Learns From Experience
- Qwen3-VL Technical Report
- $\pi_0$: A Vision-Language-Action Flow Model for General Robot Control
- RT-1: Robotics Transformer for Real-World Control at Scale
- RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control
- RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation
- PaLM-E: An Embodied Multimodal Language Model
- RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
- SRPO: Self-Referential Policy Optimization for Vision-Language-Action Models
- CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning
- RoboBrain: A Unified Brain Model for Robotic Manipulation from Abstract to Concrete
- DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
- OpenVLA: An Open-Source Vision-Language-Action Model
- Reinforcement Learning with Action Chunking
- LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning
- Vision Language Models are In-Context Value Learners
- GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
- Octo: An Open-Source Generalist Robot Policy
- Open X-Embodiment: Robotic Learning Datasets and RT-X Models
- $\pi_{0.5}$: a Vision-Language-Action Model with Open-World Generalization
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