RoboIRS: Inference-Time Internal Representation Steering for Generalist Robot Policies

Abstract:Vision-language-action (VLA) and world-action models (WAMs) often degrade under out-of-distribution task variations despite retaining partial task capability. To recover such capability, we propose RoboIRS, an inference-time internal representation steering method that uses successful and failed rollouts to train linear classifiers, select outcome-relevant intervention locations, and derive task-specific steering directions without updating policy parameters. On 15 simulation tasks with a frozen $\pi 0.5$ policy, RoboIRS improves the average success rate from 44.4% to 66.2%, outperforming alternative inference-time intervention baselines while adding little inference time. We further validate RoboIRS on real-robot manipulation using the same $\pi 0.5$ policy and demonstrate its applicability to a world-action model Cosmos Policy, where the average success rate improves from 35.4% to 55.4%. These results show that directly steering internal robot-policy representations can improve the performance of robot policies at inference time. Project website is available at this https URL.

Robotics (cs.RO)

arXiv:2610.04681 [cs.RO]

(or arXiv:2610.04681v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2610.04681 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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