[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83096-en":3,"doc-seo-83096-105":29,"detail-sidebar-cat-0-en-105":91},{"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":20,"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},83096,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",6,"Technology","WristMimic Full-Body Humanoid Control with Wrist-Guided Manipulation","Retargeting human–object interaction demonstrations to physics-based simulation requires matching body motion as well as object motion and contacts that enable successful manipulation. Position-only hand trajectories fail to encode the contact forces needed, and direct tracking can overconstrain contact-rich finger behavior. WristMimic introduces wrist-guided whole-body control that separates contact-free body motion from contact-rich hand manipulation. The approach uses kinematic pose targets for the wrist and body, while learning finger grasps from object tracking and contact outcomes, with wrist reset constraints and reward prioritization for reliable interaction. Experiments show performance comparable to finger-supervised methods and finger-agnostic retargeting across diverse hand embodiments.","arXiv :2607 .06438v2 [ cs .RO] 13 Jul 2026  \nWristMimic: Full-Body Humanoid Control with Wrist-Guided Manipulation  \nWongyun Yu 1, Youngwoon Kim 1, and Minsu Cho 1 ,2  \n1 Pohang University of Science and Technology (POSTECH)  \n2 RLWRLD  \n[https://wongyun-yu.github.io/wristmimic/](https://wongyun-yu.github.io/wristmimic/)  \nAbstract. Retargeting human–object interaction demonstrations to physics-based simulation requires reproducing not only body motion but also the object motion and contacts that make manipulation succeed.  \nHowever, position-only hand trajectories do not specify the contact forces needed to manipulate objects, and directly tracking them can overconstrain contact-rich finger behavior. We introduce WristMimic, a wristguided whole-body control framework that explicitly separates contactfree body motion from contact-rich hand manipulation. The contact-free body and wrist are guided by kinematic pose targets, whereas the fingers are not directly supervised by human hand pose. Instead, they learn grasping and manipulation behaviors from object tracking and contact outcomes. Our key insight is that the wrist is the natural gate between these two regimes. It is largely free from contact and can be tracked kinematically, yet it determines the global hand configuration and places the fingers within reachable grasp affordances. To ensure reliable wrist placement during interaction, we introduce wrist-specific reset constraints and reward prioritization. Experiments show that WristMimic matches or surpasses methods using full finger pose supervision while enabling finger-agnostic retargeting across diverse hand embodiments.  \nKeywords: Whole body control · Humanoid manipulation · Physicsbased retargeting  \n1 Introduction  \nRetargeting human-object interaction demonstrations to physics-based simulation is a long-standing problem in robotics and computer vision [10, 12, 15, 20, 28,72] . The goal of such retargeting extends beyond replicating body poses. Successful manipulation requires recovering correct object kinematics and contact dynamics in the target environment [9] . A common approach tracks kinematic trajectories from human demonstrations and replays them in simulation, which is effective for body parts that do not directly interact with the manipulated object, such as the torso, arms during reaching, and the wrist. However, for the hands, where most object interaction occurs, simply replaying kinematic trajectories is insufficient (Fig.1, left) . For hands, the objective is not merely to  \n2 W. Yu et al.  \n|  |  |\n| --- | --- |\n|  |  |\n\nFig. 1: Kinematic imitation does not guarantee successful manipulation and wrist pose encodes grasp affordance. Left: Even with accurate hand pose imitation, manipulation may fail because interaction forces are not encoded in kinematic trajectories. Top right: Wrist pose determines grasp success. Ground truth (left), feasible wrist region leading to stable grasp (middle, blue), and infeasible wrist pose causing failure (right, red) . The yellow circle illustrates the feasible wrist boundary motivating our reset strategy. Bottom right: Different wrist poses induce distinct grasp configurations, highlighting that wrist pose captures grasp affordance and enables finger-agnostic, wrist-guided manipulation.  \nreproduce human finger poses, but to reproduce the object motion and contacts that realize manipulation. However, position-based kinematic data alone cannot encode the contact forces that govern such manipulation. This observation raisesa natural question: rather than pursuing increasingly precise finger tracking, can we let the hands be guided by object and contact dynamics instead?  \nWe address this question by decoupling whole-body control into two regimes: kinematic pose-guided control for contact-free body parts and object-and contactguided control for contact-rich hand manipulation. This decoupling reflects a fundamental asymmetry in physics-based interaction. Body motion wit","cbCaisClPgp0ilxc","https://ap.wps.com/l/cbCaisClPgp0ilxc","pdf",6426793,1,25,"English","en",105,"# Introduction\n## Wrist-guided decoupled control framework\n## Wrist as the gate between regimes\n## Key contributions","[{\"question\":\"Why is position-only hand trajectory replay insufficient for successful manipulation in simulation?\",\"answer\":\"Position-only trajectories do not specify the contact forces that govern manipulation success, so kinematic replay cannot reproduce the needed contact dynamics during interaction.\"},{\"question\":\"How does WristMimic decouple whole-body control to address this limitation?\",\"answer\":\"It separates contact-free body motion, guided kinematically, from contact-rich hand manipulation, guided by object motion and contact outcomes rather than direct finger pose supervision.\"},{\"question\":\"What role does the wrist play in making the decoupling effective?\",\"answer\":\"The wrist is largely free from contact, can be tracked with kinematic guidance, and determines the global finger configuration by placing fingers within reachable grasp 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is position-only hand trajectory replay insufficient for successful manipulation in simulation?","Question",{"text":75,"@type":76},"Position-only trajectories do not specify the contact forces that govern manipulation success, so kinematic replay cannot reproduce the needed contact dynamics during interaction.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does WristMimic decouple whole-body control to address this limitation?",{"text":80,"@type":76},"It separates contact-free body motion, guided kinematically, from contact-rich hand manipulation, guided by object motion and contact outcomes rather than direct finger pose supervision.",{"name":82,"@type":73,"acceptedAnswer":83},"What role does the wrist play in making the decoupling effective?",{"text":84,"@type":76},"The wrist is largely free from contact, can be tracked with kinematic guidance, and determines the global finger configuration by placing fingers within reachable grasp 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