How to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference
Overview
This research presents a learning framework for fine-grained manipulation tasks characterized by implicit success criteria, using knife peeling as a case study. The approach employs a two-stage pipeline: first, a robust initial policy is learned via force-aware data collection and imitation learning to generalize across object variations; second, the policy is refined through preference-based fine-tuning using a reward model that integrates quantitative metrics with qualitative human feedback. Using only 50 to 200 peeling trajectories, the system achieves 100% success on cucumbers, apples, and potatoes, with performance improvements of up to 40% after fine-tuning. Policies trained on one produce category also transfer zero-shot to held-out categories, achieving 50% success on zucchini, 90% on pear, and 80% on daikon radish.
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“arXiv:2603.03280v2 Announce Type: replace Abstract: Many essential manipulation tasks - such as food preparation, surgery, and craftsmanship - remain difficult for autonomous robots. These tasks are characterized not only by contact-rich, force-sensitive dynamics, but also by their "implicit" success criteria: unlike pick-and-place, task quality in these domains is continuous and subjective (e.g. how well a potato is”
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- arXiv Robotics — research abstractsSignalHow to Peel with a Knife: Aligning Fine-Grained Manipulation with Human Preference
This research presents a learning framework for fine-grained manipulation tasks characterized by implicit success criteria, using knife peeling as a case study. The approach employs a two-stage pipeline: first, a robust initial policy is learned via force-aware data collection and imitation learning to generalize across object variations; second, the policy is refined through preference-based fine-tuning using a reward model that integrates quantitative metrics with qualitative human feedback. Using only 50 to 200 peeling trajectories, the system achieves 100% success on cucumbers, apples, and potatoes, with performance improvements of up to 40% after fine-tuning. Policies trained on one produce category also transfer zero-shot to held-out categories, achieving 50% success on zucchini, 90% on pear, and 80% on daikon radish.
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