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Results show that blended shared control leads to higher autonomous performance compared to typical methods for fine-tuning robot policies from fully teleoperated interventions.
A controlled empirical study of which design choices allow visuomotor policies to generalize across viewpoints finds that viewpoint generalization improves when dense visual tokens are retained and the action head participates in geometric reasoning.
This work introduces SeeQ (Subtask-elicited Q-functions), which instead learns Q-values for the currently active subtask, which substantially improves best-of-N policy steering on real-world manipulation tasks.
This paper introduces a simple post-training recipe that turns off-the-shelf VLMs into robotic reasoners, and suggests that free-form language reasoning can function as a test-time compute mechanism for steering low-level policies.
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