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This research introduces a novel flow control technique for Vision-Language-Action (VLA) models that allows real-time steering of robotic actions via simple inputs like keyboard commands without requiring retraining. By treating user input as an initial condition in the model's deterministic flow, the method achieves high fidelity to user intent while maintaining conformity with the model's learned action distribution. The approach significantly boosts task success rates and demonstrates that fine-tuning on these controlled trajectories further improves autonomous policy performance.
Flow Control: Steering Vision-Language-Action Models with Simple Real-Time Inputs
arXiv:2606.10180v2 Announce Type: replace Abstract: We introduce flow control of vision-language-action (VLA) models, a simple and effective way to steer VLA actions in real time through generic inputs, such as a keyboard. This method can be used out-of-the-box and does not require retraining or fine-tuning VLAs. It enables relatively crude user inputs to steer a VLA to align with user intent. Flow control writes the user input into the initial condition of the VLA's flow matching action expert. Because the flow is deterministic, integration retains information about the user input while moving it toward the action distribution learned during training, so that the generated actions reflect the user's intent (high fidelity) and stay close to the VLA's own unsteered actions (high conformity). We demonstrate that flow control has many desirable properties: (1) flow control accurately and responsively steers robot actions with user inputs, (2) it is robust to suboptimal user inputs, (3) it enables users to steer VLAs to achieve significantly higher success rates and faster task completion, and (4) fine-tuning a VLA on flow control trajectories improves the autonomous policy. Together, these results provide a simple and intuitive way for users to help steer VLA actions, increasing task performance. Project page and videos: https://jcykao.github.io/flowcontrol/.
Abstract:We introduce flow control of vision-language-action (VLA) models, a simple and effective way to steer VLA actions in real time through generic inputs, such as a keyboard. This method can be used out-of-the-box and does not require retraining or fine-tuning VLAs. It enables relatively crude user inputs to steer a VLA to align with user intent. Flow control writes the user input into the initial condition of the VLA's flow matching action expert. Because the flow is deterministic, integration retains information about the user input while moving it toward the action distribution learned during training, so that the generated actions reflect the user's intent (high fidelity) and stay close to the VLA's own unsteered actions (high conformity). We demonstrate that flow control has many desirable properties: (1) flow control accurately and responsively steers robot actions with user inputs, (2) it is robust to suboptimal user inputs, (3) it enables users to steer VLAs to achieve significantly higher success rates and faster task completion, and (4) fine-tuning a VLA on flow control trajectories improves the autonomous policy. Together, these results provide a simple and intuitive way for users to help steer VLA actions, increasing task performance. Project page and videos: this https URL.
Accepted to the 10th Conference on Robot Learning (CoRL 2026). 29 pages. Project page: this https URL
Robotics (cs.RO); Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
arXiv:2606.10180 [cs.RO]
(or arXiv:2606.10180v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.10180 Focus to learn more arXiv-issued DOI via DataCite
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