Self-Labeled Preference Transfer for Steerable Robot Manipulation
Overview
This research proposes Self-Labeled Preference Transfer (SPT), a method that allows robot policies to adapt to new tasks without requiring manual preference annotations. SPT uses a shared vision-language backbone to recognize preferences from annotated source demonstrations and self-labels target demonstrations for fine-tuning. In simulation, SPT achieves 84.8% preference following and 89.2% task success, compared to 59.4% and 88.0% for naive fine-tuning. In real-world experiments, SPT improves preference following from 52.5% to 92.5% relative to naive fine-tuning across two task transfers.
From arXiv Robotics — research abstracts
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Reported quantity · Reported accuracy: 96.4 percent · Basis not reported
“a shared vision-language backbone for preference-conditioned action prediction and preference recognition on annotated source demonstrations. It then freezes the recognizer to preserve its cross-task preference recognition capability and uses it to self-label target demonstrations for policy fine-tuning. Across five types of execution preferences in simulation, the recognizer achieves 96.4% accuracy, and SPT reaches”
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- 2026-10-31T04:11:05.274Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalSelf-Labeled Preference Transfer for Steerable Robot Manipulation
This research proposes Self-Labeled Preference Transfer (SPT), a method that allows robot policies to adapt to new tasks without requiring manual preference annotations. SPT uses a shared vision-language backbone to recognize preferences from annotated source demonstrations and self-labels target demonstrations for fine-tuning. In simulation, SPT achieves 84.8% preference following and 89.2% task success, compared to 59.4% and 88.0% for naive fine-tuning. In real-world experiments, SPT improves preference following from 52.5% to 92.5% relative to naive fine-tuning across two task transfers.
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