aiqbench analysis
This paper introduces UF-OPS, a technique that enhances the reliability of behavior cloning in robotics by using verifiers to steer action selection without modifying base model parameters. By predicting action success likelihood during execution, the method achieves a significant 49% improvement in task completion rates across real-world scenarios. This advancement addresses critical brittleness issues in current manipulation policies, offering a lightweight solution for deploying robust AI agents.
Update-Free On-Policy Steering via Verifiers
arXiv:2603.10282v3 Announce Type: replace Abstract: In recent years, Behavior Cloning (BC) has become one of the most prevalent methods for learning manipulation from human demonstrations. Despite their successes, BC policies are often brittle and struggle with precise manipulation. To overcome these issues, we propose UF-OPS, an Update-Free On-Policy Steering method that enables the robot to predict the success likelihood of its actions and adapt its strategy at execution time. We accomplish this by training verifier functions using policy rollout data obtained during an initial evaluation of the policy. These verifiers are subsequently used to steer the base policy toward actions with a higher likelihood of success. Our method improves the performance of black-box diffusion policies, without changing the base parameters, making it lightweight and flexible. We present results from both simulation and real-world data and achieve an average 49% improvement in success rate over the base policy across 5 real tasks.
Abstract:In recent years, Behavior Cloning (BC) has become one of the most prevalent methods for learning manipulation from human demonstrations. Despite their successes, BC policies are often brittle and struggle with precise manipulation. To overcome these issues, we propose UF-OPS, an Update-Free On-Policy Steering method that enables the robot to predict the success likelihood of its actions and adapt its strategy at execution time. We accomplish this by training verifier functions using policy rollout data obtained during an initial evaluation of the policy. These verifiers are subsequently used to steer the base policy toward actions with a higher likelihood of success. Our method improves the performance of black-box diffusion policies, without changing the base parameters, making it lightweight and flexible. We present results from both simulation and real-world data and achieve an average 49% improvement in success rate over the base policy across 5 real tasks.
Robotics (cs.RO)
arXiv:2603.10282 [cs.RO]
(or arXiv:2603.10282v3 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.10282 Focus to learn more arXiv-issued DOI via DataCite
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Full text under the publisher's open licence · 237 words
