这篇论文展示了如何用视觉验证器让机器人策略在推理时自我改进，无需额外训练，效率堪比专家演示，值得关注。
论文提出了VERITAS框架，将预训练通用机器人策略作为“生成器”，搭配无梯度的“视觉验证器”在推理时评估动作。该框架无需额外训练即可提升策略性能，优于原通用策略。使用验证的自主轨迹进行微调后，策略性能持续提升，且效率与专家演示相当，无需人工干预。实验表明推理时验证是一种实用且可扩展的部署改进机制。
Visual Verification Enables Inference-time Steering and Autonomous Policy Improvement
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.