[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85277-en":3,"doc-seo-85277-105":28,"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":13,"seo_description":14,"update_tm":26,"read_time":27},85277,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Whole-Body Semantic-to-Actuation Grounding of Elephant-Inspired Soft-Trunk Motion via Lightweight Flow Matching","Close-contact human–robot interaction can use trunk-like continuum manipulators to express diverse, whole-body, shape-based behaviors, but mapping open-vocabulary language to feasible motions is difficult because end-effector representations underspecify expression while direct body-shape specification is too high-dimensional. A whole-body semantic-to-actuation grounding framework is proposed for elephant-inspired soft-trunk motion. Open-vocabulary responses are canonicalized into morphology-aligned intent–intensity tuples, then executable tendon-actuation trajectories are generated via compact Catmull–Rom spline controls sampled by a lightweight rectified-flow generator. Evaluations show large improvements in grounding correctness and faster inference, with higher user satisfaction in a 100-participant physical HRI study.","Whole-Body Semantic-to-Actuation Grounding of Elephant-Inspired Soft-Trunk Motion via Lightweight Flow Matching  \nTingcong Liu 1 ,2 , Tongshun Chen3 , Siyi Ma4 , Yuhao Wang3 , Aye Phyu Phyu Aung2 , Ibrahim Alsarraj3 ,  \nJ. Senthilnath2 , Bo An 1 , Ke Wu3  \narXiv :2607 . 11018v1 [ cs .RO] 13 Jul 2026  \nAbstract—For close-contact human–robot interaction (HRI), trunk-like continuum manipulators offer an attractive physical channel for diverse whole-body shape-based expression. However, directly applying conventional vision–language–action (VLA) grounding to such systems is challenging: tip- or endeffector motion underspecifies whole-body expression, whereas direct whole-body specification is too high-dimensional for semantic planning and difficult to keep feasible. To address this representation mismatch, we propose a whole-body semanticto-actuation grounding framework for elephant-inspired softtrunk HRI via lightweight flow matching. The framework grounds open-vocabulary responses produced by a multimodal large language model (MLLM) into executable tendonactuation trajectories that realize distributed continuum-body motion. It first canonicalizes open-vocabulary responses into bounded morphology-aligned intent–intensity tuples that define executable motion families, and then parameterizes continuous whole-body tendon-actuation profiles with compact Catmull– Rom spline controls. Conditioned on the resulting tuple, a lightweight rectified-flow generator learns a distribution over feasible spline controls, enabling efficient one-to-many sampling of smooth and socially valid trunk-motion realizations. We evaluate the framework through semantic-to-actuation grounding ablations and a 100-participant physical HRI study. In the experiments, the proposed framework improves held-out grounding correctness from 25.0% to 77.2% over a rawresponse dense-regression baseline. Compared with a denoisingdiffusion baseline, rectified flow improves correctness from 71.9% to 77.2% and reduces inference time from 7.86 ms to 4.87 ms, while preserving motion diversity. In the physical HRI study, the proposed framework increases the positive overallsatisfaction rating from 46% to 82% over the audiovisual-only baseline, demonstrating the user-facing benefit of the generated soft-trunk motion channel.  \nKeywords: Soft robotics; Whole-body Semantic-toactuation Grounding; Bio-inspiration; MLLM; HRI.  \nI. INTRODUCTION  \nClose-contact human–robot interaction (HRI) brings the robot body into the user’s interpersonal and physical space, where approach distance, bodily behavior, and touch shape perceived appropriateness, comfort, intent, and safety [1],[2] . Since robot motion can function as a communicative resource beyond speech or screen-based display [3], closecontact HRI motivates a physical extension that can act asan embodied channel for social response. Rigid manipulators offer accurate and repeatable motion, but close-contact social use must also address physical safety, perceived safety, and comfort in proximity [4] . Trunk-like soft continuum manipulators are therefore promising: their continuum bodies can generate non-contact body language through approach, withdrawal, and gesture [5], while their compliant morphol-  \nogy can deform during light contact [6] . However, the same high-dimensional and deformable whole-body morphology that enables expressive and contact-friendly interaction also changes the grounding problem: the behavior perceived by the user is distributed over the entire continuum body rather than concentrated at a single end effector.  \nThis creates a mismatch with the tip-centered action representations commonly used in vision-language-action robot policies [7] . Existing soft-robot VLA-style formulations [8] typically represent robot actions through endeffector displacements or low-level control commands. Such representations are suitable for manipulation tasks that demand precise task-space outcomes, but are insufficient for soft tru","cbCaidXE5hocgWs9","https://ap.wps.com/l/cbCaidXE5hocgWs9","pdf",2849062,1,"English","en",105,"# Abstract\n# Introduction\n## Motivation and representation mismatch\n## Proposed semantic-to-actuation grounding interface\n## Challenges for executability and distributional generation\n## Why rectified flow for efficient sampling","[{\"question\":\"What benefits were observed in the physical human–robot interaction study?\",\"answer\":\"Compared with an audiovisual-only baseline, the framework increased the positive overall satisfaction rating from 46% to 82%, indicating user-facing improvement from the generated soft-trunk motion channel.\"}]",1784202220,20,{"code":4,"msg":29,"data":30},"ok",{"site_id":23,"language":22,"slug":31,"title":13,"keywords":32,"description":14,"schema_data":33,"social_meta":77,"head_meta":79,"extra_data":81,"updated_unix":26},"whole-body-semantic-to-actuation-grounding-of-elephant-inspired-soft-trunk-motion-via-lightweight-flow-matching","",{"@graph":34,"@context":76},[35,52,67],{"@type":36,"itemListElement":37},"BreadcrumbList",[38,42,46,49],{"item":39,"name":40,"@type":41,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":43,"name":44,"@type":41,"position":45},"https://docshare.wps.com/document/","Document",2,{"item":47,"name":12,"@type":41,"position":48},"https://docshare.wps.com/document/research-report/",3,{"item":50,"name":13,"@type":41,"position":51},"https://docshare.wps.com/document/whole-body-semantic-to-actuation-grounding-of-elephant-inspired-soft-trunk-motion-via-lightweight-flow-matching/85277/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":61,"encodingFormat":59,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":39,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-07-17","2026-07-16",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70],{"name":71,"@type":72,"acceptedAnswer":73},"What benefits were observed in the physical human–robot interaction study?","Question",{"text":74,"@type":75},"Compared with an audiovisual-only baseline, the framework increased the positive overall satisfaction rating from 46% to 82%, indicating user-facing improvement from the generated soft-trunk motion channel.","Answer","https://schema.org",{"og:url":50,"og:type":78,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":80,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":83},[84,88,92,96,101,106,111,114,118,121,125],{"id":20,"doc_module":4,"doc_module_name":44,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":45,"doc_module":4,"doc_module_name":44,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":44,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":44,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":44,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":44,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":44,"category_name":12,"show_sort_weight":112,"slug":113},30,"research-report",{"id":115,"doc_module":4,"doc_module_name":44,"category_name":116,"show_sort_weight":27,"slug":117},9,"Religion & Spirituality","religion-spirituality",{"id":27,"doc_module":4,"doc_module_name":44,"category_name":119,"show_sort_weight":27,"slug":120},"World Cup","world-cup",{"id":122,"doc_module":4,"doc_module_name":44,"category_name":123,"show_sort_weight":122,"slug":124},10,"Lifestyle","lifestyle",{"id":126,"doc_module":4,"doc_module_name":44,"category_name":127,"show_sort_weight":97,"slug":128},19,"General","general"]