  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.
