A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning

• URL: http://arxiv.org/abs/2509.15937v1
• Date: Fri, 19 Sep 2025 12:44:29 GMT
• Title: A Vision-Language-Action-Critic Model for Robotic Real-World Reinforcement Learning
• Authors: Shaopeng Zhai, Qi Zhang, Tianyi Zhang, Fuxian Huang, Haoran Zhang, Ming Zhou, Shengzhe Zhang, Litao Liu, Sixu Lin, Jiangmiao Pang,
• Abstract summary: We introduce VLAC, a general process reward model built upon InternVL.<n>It outputs dense progress delta and done signal, eliminating task-specific reward engineering.<n>VLAC is trained on vision-language datasets to strengthen perception, dialogic and reasoning capabilities.
• Score: 26.546473157595482
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Robotic real-world reinforcement learning (RL) with vision-language-action (VLA) models is bottlenecked by sparse, handcrafted rewards and inefficient exploration. We introduce VLAC, a general process reward model built upon InternVL and trained on large scale heterogeneous datasets. Given pairwise observations and a language goal, it outputs dense progress delta and done signal, eliminating task-specific reward engineering, and supports one-shot in-context transfer to unseen tasks and environments. VLAC is trained on vision-language datasets to strengthen perception, dialogic and reasoning capabilities, together with robot and human trajectories data that ground action generation and progress estimation, and additionally strengthened to reject irrelevant prompts as well as detect regression or stagnation by constructing large numbers of negative and semantically mismatched samples. With prompt control, a single VLAC model alternately generating reward and action tokens, unifying critic and policy. Deployed inside an asynchronous real-world RL loop, we layer a graded human-in-the-loop protocol (offline demonstration replay, return and explore, human guided explore) that accelerates exploration and stabilizes early learning. Across four distinct real-world manipulation tasks, VLAC lifts success rates from about 30\% to about 90\% within 200 real-world interaction episodes; incorporating human-in-the-loop interventions yields a further 50% improvement in sample efficiency and achieves up to 100% final success.

Related papers

• Self-Correcting VLA: Online Action Refinement via Sparse World Imagination [55.982504915794514]
  We propose Self-Correcting VLA (SC-VLA), which achieve self-improvement by intrinsically guiding action refinement through sparse imagination.<n>SC-VLA achieve state-of-the-art performance, yielding the highest task throughput with 16% fewer steps and a 9% higher success rate than the best-performing baselines.
  arXiv  Detail & Related papers  (2026-02-25T06:58:06Z)
• TOPReward: Token Probabilities as Hidden Zero-Shot Rewards for Robotics [46.912038830356714]
  We introduce TOPReward, a novel, probabilistically grounded temporal value function that estimates robotic task progress.<n>In zero-shot evaluations across 130+ distinct real-world tasks, TOPReward achieves 0.947 mean Value-Order Correlation (VOC) on Qwen3-VL.<n>We demonstrate that TOPReward serves as a versatile tool for downstream applications, including success detection and reward-aligned behavior cloning.
  arXiv  Detail & Related papers  (2026-02-22T19:25:48Z)
• Global Prior Meets Local Consistency: Dual-Memory Augmented Vision-Language-Action Model for Efficient Robotic Manipulation [95.89924101984566]
  We introduce OptimusVLA, a dual-memory VLA framework with Global Prior Memory (GPM) and Local Consistency Memory (LCM)<n>GPM replaces Gaussian noise with task-level priors retrieved from semantically similar trajectories.<n>LCM injects a learned consistency constraint that enforces temporal coherence and smoothness of trajectory.
  arXiv  Detail & Related papers  (2026-02-22T15:39:34Z)
• RoboGene: Boosting VLA Pre-training via Diversity-Driven Agentic Framework for Real-World Task Generation [37.52152452548065]
  RoboGene is an agentic framework designed to automate the generation of diverse, physically plausible manipulation tasks.<n>We conduct extensive quantitative analysis and large-scale real-world experiments, collecting datasets of 18k trajectories.<n>Results demonstrate that RoboGene significantly outperforms state-of-the-art foundation models.
  arXiv  Detail & Related papers  (2026-02-18T13:29:43Z)
• EVOLVE-VLA: Test-Time Training from Environment Feedback for Vision-Language-Action Models [57.75717492488268]
  Vision-Language-Action (VLA) models have advanced robotic manipulation by leveraging large language models.<n>Supervised Finetuning (SFT) requires hundreds of demonstrations per task, rigidly memorizing trajectories, and failing to adapt when deployment conditions deviate from training.<n>We introduce EVOLVE-VLA, a test-time training framework enabling VLAs to continuously adapt through environment interaction with minimal or zero task-specific demonstrations.
  arXiv  Detail & Related papers  (2025-12-16T18:26:38Z)
• PvP: Data-Efficient Humanoid Robot Learning with Proprioceptive-Privileged Contrastive Representations [30.986538644112105]
  Whole-body control (WBC) is essential for enabling humanoid robots to perform complex tasks in dynamic environments.<n>We propose a Proprioceptive-Privileged contrastive learning framework that leverages the intrinsic complementarity between proprioceptive and privileged states.<n>We develop SRL4Humanoid, the first unified and modular framework that provides high-quality implementations of representative state representation learning (SRL) methods for humanoid robot learning.
  arXiv  Detail & Related papers  (2025-12-15T08:50:20Z)
• VLA-RL: Towards Masterful and General Robotic Manipulation with Scalable Reinforcement Learning [14.099306230721245]
  We present VLA-RL, an exploration-based framework that improves on online collected data at test time.<n>We fine-tune a pretrained vision-language model as a robotic process reward model, which is trained on pseudo reward labels annotated on automatically extracted task segments.<n>VLA-RL enables OpenVLA-7B to surpass the strongest finetuned baseline by 4.5% on 40 challenging robotic manipulation tasks in LIBERO.
  arXiv  Detail & Related papers  (2025-05-24T14:42:51Z)
• From Seeing to Doing: Bridging Reasoning and Decision for Robotic Manipulation [35.79160868966466]
  We propose FSD (From Seeing to Doing), a novel vision-language model that generates intermediate representations through spatial relationship reasoning.<n>Our approach combines a hierarchical data pipeline for training with a self-consistency mechanism that aligns spatial coordinates with visual signals.<n>We show that FSD achieves 40.6% success rate in SimplerEnv and 72% success rate across 8 real-world tasks, outperforming the strongest baseline by 30%.
  arXiv  Detail & Related papers  (2025-05-13T13:20:46Z)
• Vision Language Models are In-Context Value Learners [89.29486557646624]
  We present Generative Value Learning (GVL), a universal value function estimator that leverages the world knowledge embedded in vision-language models (VLMs) to predict task progress. Without any robot or task specific training, GVL can in-context zero-shot and few-shot predict effective values for more than 300 distinct real-world tasks.
  arXiv  Detail & Related papers  (2024-11-07T09:17:50Z)
• Affordance-Guided Reinforcement Learning via Visual Prompting [51.361977466993345]
  Keypoint-based Affordance Guidance for Improvements (KAGI) is a method leveraging rewards shaped by vision-language models (VLMs) for autonomous RL.<n>On diverse real-world manipulation tasks specified by natural language descriptions, KAGI improves the sample efficiency of autonomous RL and enables successful task completion in 30K online fine-tuning steps.
  arXiv  Detail & Related papers  (2024-07-14T21:41:29Z)
• A Framework for Efficient Robotic Manipulation [79.10407063260473]
  We show that a single robotic arm can learn sparse-reward manipulation policies from pixels. We show that, given only 10 demonstrations, a single robotic arm can learn sparse-reward manipulation policies from pixels.
  arXiv  Detail & Related papers  (2020-12-14T22:18:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.