{"ID":6138302,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T14:35:24.647767831Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07491","arxiv_id":"2607.07491","title":"Smooth Operator: A Real-Time Sampling-Based Algorithm for Kinematic Hand Retargeting","abstract":"Advances in learning-based robotic manipulation, such as Vision-Language-Action (VLA) models and Video Action Models (VAMs), heavily rely on high-quality teleoperation data. Their capabilities are strictly upper-bounded by the quality of the underlying human demonstrations. Current gradient-based retargeting algorithms often converge to different local minima, resulting in jitter that affects data quality and teleoperation experience. To address this, we introduce the Sampling-Based Retargeter (SBR), a novel gradient-free retargeting method drawn from the rich literature of sampling-based control and explicitly designed for low-jitter, real-time kinematic retargeting. We evaluate SBR both in simulation and through a rigorous real-world user study involving 18 participants performing 3 complex manipulation tasks. Compared to gradient-based baselines, SBR achieved the highest overall task success rate (54.1%) while significantly reducing operator cognitive fatigue, recording the lowest NASA-TLX workload score (36.4 out of 100). Ultimately, we establish SBR as a highly effective, intuitive retargeter for dexterous manipulation, providing the community with a rigorous benchmarking methodology to guide future retargeting research.","short_abstract":"Advances in learning-based robotic manipulation, such as Vision-Language-Action (VLA) models and Video Action Models (VAMs), heavily rely on high-quality teleoperation data. Their capabilities are strictly upper-bounded by the quality of the underlying human demonstrations. Current gradient-based retargeting algorithms...","url_abs":"https://arxiv.org/abs/2607.07491","url_pdf":"https://arxiv.org/pdf/2607.07491v1","authors":"[\"Robert Jomar Malate\",\"Erik Bauer\",\"Norica Bacuieti\",\"Stefanos Charalambous\",\"Elvis Nava\",\"Robert K. Katzschmann\",\"Benedek Forrai\"]","published":"2026-07-08T14:50:17Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}