通过具身思维链推理进行机器人控制
Robotic Control via Embodied Chain-of-Thought Reasoning
AI总结：
针对视觉-语言-动作模型（VLAs）泛化能力不足且缺乏感官与状态基础推理的问题，提出具身思维链推理（ECoT）方法，训练模型在执行动作前对计划、子任务及视觉特征进行多步推理，使OpenVLA在泛化任务上的绝对成功率提升28%，并支持人类通过自然语言解释和纠正策略失败。
摘要：
学习型机器人控制策略的一个关键局限在于其无法在训练数据之外进行泛化。最近关于视觉-语言-动作模型（VLAs）的研究表明，将大型、互联网预训练的视觉-语言模型作为学习型机器人策略的主干，可以大幅提升其鲁棒性和泛化能力。然而，大型视觉-语言模型在其他领域最令人兴奋的能力之一是它们能够迭代地推理解答复杂问题。能否将这种能力引入机器人领域，使策略在行动前通过对给定任务的推理来提升性能？由于标准VLAs可用的训练样本相对简单，朴素地使用“思维链”风格的提示在标准VLAs中效果显著较差。此外，常规CoT中常见的对子任务的纯语义推理，对于需要将其推理建立在感官观测和机器人状态基础上的机器人策略而言是不够的。为此，我们为VLAs引入了具身思维链推理，训练VLAs在预测机器人动作之前，执行多个步骤的推理，包括关于计划、子任务、运动以及视觉基础特征（如物体边界框和末端执行器位置）的推理。我们设计了一个可扩展的流程，用于在大型机器人数据集上为ECoT生成合成训练数据。我们证明，ECoT在具有挑战性的泛化任务中，将当前最强的开源VLA策略OpenVLA的绝对成功率提高了28%，且无需任何额外的机器人训练数据。此外，ECoT使人类更容易解释策略的失败，并使用自然语言纠正其行为。
英文摘要：
A key limitation of learned robot control policies is their inability to generalize outside their training data. Recent works on vision-language-action models (VLAs) have shown that the use of large, internet pre-trained vision-language models as the backbone of learned robot policies can substantially improve their robustness and generalization ability. Yet, one of the most exciting capabilities of large vision-language models in other domains is their ability to reason iteratively through complex problems. Can that same capability be brought into robotics to allow policies to improve performance by reasoning about a given task before acting? Naive use of "chain-of-thought" (CoT) style prompting is significantly less effective with standard VLAs because of the relatively simple training examples that are available to them. Additionally, purely semantic reasoning about sub-tasks, as is common in regular CoT, is insufficient for robot policies that need to ground their reasoning in sensory observations and the robot state. To this end, we introduce Embodied Chain-of-Thought Reasoning (ECoT) for VLAs, in which we train VLAs to perform multiple steps of reasoning about plans, sub-tasks, motions, and visually grounded features like object bounding boxes and end effector positions, before predicting the robot action. We design a scalable pipeline for generating synthetic training data for ECoT on large robot datasets. We demonstrate, that ECoT increases the absolute success rate of OpenVLA, the current strongest open-source VLA policy, by 28% across challenging generalization tasks, without any additional robot training data. Additionally, ECoT makes it easier for humans to interpret a policy's failures and correct its behavior using natural language.
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