aiqbench analysis
This research introduces VERITAS, a generator-verifier framework that leverages visual verification for inference-time steering in robotic policies. By evaluating actions at runtime without additional training or human intervention, the system enables autonomous self-improvement using verified rollouts as supervision signals. The approach demonstrates significant performance gains over standard generalist models and offers an efficient alternative to expert demonstrations for offline policy refinement.
Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement
arXiv:2606.18247v1 Announce Type: new Abstract: Robots deployed in the real world should learn from their experience and improve over time. This requires a mechanism of practicing and learning from feedback. In this paper, we propose VERITAS, a generator-verifier framework for generalist robot policies for inference-time policy steering and self-improvement. We use a pre-trained generalist robot policy as a ``generator'' and pair it with a gradient-free ``visual verifier'' that evaluates actions at inference time. This framework enables inference-time steering that improves policy performance without additional training. We demonstrate that inference-time verification consistently outperforms vanilla generalists without training on additional demonstration data. Additionally, we demonstrate that the verified rollouts provide effective supervision for offline policy improvement: policies fine-tuned on verified self-generated trajectories achieve consistent performance gains. Notably, we find that post-training with verified rollouts achieves comparable efficiency to expert demonstrations, while requiring no human interventions. Our results highlight inference-time verification as a practical and scalable mechanism for improving robotic policies during deployment.
Abstract:Robots deployed in the real world should learn from their experience and improve over time. This requires a mechanism of practicing and learning from feedback. In this paper, we propose VERITAS, a generator-verifier framework for generalist robot policies for inference-time policy steering and self-improvement. We use a pre-trained generalist robot policy as a ``generator'' and pair it with a gradient-free ``visual verifier'' that evaluates actions at inference time. This framework enables inference-time steering that improves policy performance without additional training. We demonstrate that inference-time verification consistently outperforms vanilla generalists without training on additional demonstration data. Additionally, we demonstrate that the verified rollouts provide effective supervision for offline policy improvement: policies fine-tuned on verified self-generated trajectories achieve consistent performance gains. Notably, we find that post-training with verified rollouts achieves comparable efficiency to expert demonstrations, while requiring no human interventions. Our results highlight inference-time verification as a practical and scalable mechanism for improving robotic policies during deployment.
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2606.18247 [cs.RO]
(or arXiv:2606.18247v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.18247 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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