[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84236-en":3,"doc-seo-84236-105":29,"detail-sidebar-cat-0-en-105":82},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},84236,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Smooth Operator: A Real-Time Sampling-Based Algorithm for Kinematic Hand Retargeting","Advances in learning-based robotic manipulation, such as VisionLanguage-Action (VLA) models and Video Action Models (VAMs), depend on high-quality teleoperation data. That capability is constrained by the fidelity of human demonstrations, and gradient-based retargeting often converges to different local minima, producing jitter that harms data quality and operator experience. This work introduces the Sampling-Based Retargeter (SBR), a gradient-free, low-jitter real-time method, evaluated in simulation and a real-world user study with 18 participants on three tasks.","Smooth Operator: A Real-Time Sampling-Based Algorithm for Kinematic Hand Retargeting  \nRobert Jomar Malate 1 ,3 ,∗ Erik Bauer2 Norica Bacuieti3 Stefanos Charalambous3 Elvis Nava3 Robert K. Katzschmann 1 ,3 ,∗ Benedek Forrai3 ,∗  \n1ETH Zurich, Switzerland 2 Stanford University, United States 3mimic robotics, Switzerland  \narXiv :2607 .0749 1v 1 [ cs .RO] 8 Jul 2026  \n* Corresponding authors: [rmalate@student.ethz.ch](rmalate@student.ethz.ch),  \n[rkk@ethz.ch](rkk@ethz.ch), [and](and benedek.forrai@mimicrobotics.com)[ benedek.forrai@mimicrobotics.com](and benedek.forrai@mimicrobotics.com)  \n[https://mimicrobotics.github.io/smooth-operator/](https://mimicrobotics.github.io/smooth-operator/)  \nFigure 1: Overview. Drawing from the rich traditions of sampling-based control in robotics, we introduce a sampling-based retargeter for tracking human hand pose commands with dexterous robotic hands (a) . We rigorously evaluate its performance against the state of the art along several metrics, both in a simulated environment (b) that allows quick iteration, and in a real-world user study involving 18 participants on 3 complex manipulation tasks (c) . We find that our novel sampling based method leads to a better user experience, reduced jitter, and achieves state of the art performance in task success rates and operator workload scores.  \nAbstract: Advances in learning-based robotic manipulation, such as VisionLanguage-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.  \nKeywords: Teleoperation, Dexterous Manipulation, Retargeting, Sampling  \nBased Control  \n1 Introduction  \nRetargeting algorithms enable humans to bridge the human-robot embodiment gap, providing a method to control and teach robots to perform dexterous behaviors autonomously. Defined as the mapping of intent from a source to a target [1], retargeting is a critical component of the robot learning data flywheel, providing a mechanism for extracting human manipulation data through real-time control [2, 3, 4] or offline processing [5, 6] . We call the former and latter online and offline retargeting, respectively. However, retargeting is challenging due to the complex and nonlinear mapping between the human and robot hand manifolds [7] . This complexity arises from the kinematic discrepancies between the embodiment gap. To address the embodiment gap, a wide variety of approaches have been developed to translate human hand motion into executable robot commands [8] .  \nThese retargeting approaches commonly use gradient-based optimization, which relies on local derivative information to map human intent to robot joints. Gradient solvers, however, risk converging into undesirable local minima, thereby generating aggressive, high-frequency command spikes (jitter) . In real-world teleoperation, this erratic behavior degrades grasp stability and makes aggressive low-pass filtering ne","cbCaidepnzltS71R","https://ap.wps.com/l/cbCaidepnzltS71R","pdf",21991013,1,23,"English","en",105,"# Introduction\n## Problem with gradient-based retargeting\n## Evaluation challenges and experimental design\n## Main contribution: Sampling-Based Retargeter (SBR)","[{\"question\":\"What benefit does smoother retargeting provide for operators?\",\"answer\":\"Smoother retargeting reduces jitter and operator cognitive fatigue, improving task success while lowering workload as measured by NASA-TLX.\"}]",1784194260,58,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":77,"head_meta":79,"extra_data":81,"updated_unix":27},"smooth-operator-a-real-time-sampling-based-algorithm-for-kinematic-hand-retargeting","",{"@graph":35,"@context":76},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/smooth-operator-a-real-time-sampling-based-algorithm-for-kinematic-hand-retargeting/84236/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"What benefit does smoother retargeting provide for operators?","Question",{"text":74,"@type":75},"Smoother retargeting reduces jitter and operator cognitive fatigue, improving task success while lowering workload as measured by NASA-TLX.","Answer","https://schema.org",{"og:url":51,"og:type":78,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":80,"canonical":51},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":83},[84,88,92,96,101,106,111,114,119,122,126],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":45,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":45,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":45,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general"]