[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-83228-en":3,"doc-seo-83228-105":28,"detail-sidebar-cat-0-en-105":90},{"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},83228,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","HUMAIN: Human-Aware Implicit Social Robot Navigation","Effective social robot navigation depends on sensitivity to human behavior that is often embedded in subtle skeletal cues such as gait and orientation. HUMAIN proposes a framework that injects implicit social cues into the planning loop using knowledge distillation. A transformer-based teacher fuses historic images, skeletal keypoints, robot state, and goal to learn human-aware representations. A lightweight student is distilled for real-time inference from minimal inputs, aligning latent features and trajectory reconstruction. Experiments show an average 29.8% improvement over state-of-the-art baselines.","HUMAIN: Human-Aware Implicit Social Robot Navigation  \nDaeun Song 1 , Nhat Le2 , Jeffrey Chen3 , Mohammad Nazeri2 , Amirreza Payandeh2 , Rohan Chandra3 , Reuth Mirsky4 , Ross Mead5 , Ling Xiao6 , Xuesu Xiao2  \narXiv :2607 .07357v 1 [ cs .RO] 8 Jul 2026  \nAbstract—Effective social robot navigation requires sensitivity to human behavior, often revealed through subtle skeletal cues like gait and orientation. We present Human-Aware Implicit Social Robot Navigation (HUMAIN), a novel framework that fuses implicit social cues directly into the planning loop via knowledge distillation. We first employ a transformer-based teacher model that fuses rich multi-modal inputs, including historic images, skeletal keypoints, robot state, and a robot’starget goal, to learn robust, human-aware representations for the robot’s future trajectory planning. To enable real-time deployment, we then distill this knowledge into a lightweight student model. By optimizing for both trajectory reconstruction and latent feature alignment with the teacher, the student learns to infer complex social dynamics from minimal inputs. Bridging the prediction–planning gap with an efficient distilled architecture, our method enables robots to reason about human behavior in a manner that is adaptive, robust, and socially compliant. We validate HUMAIN through extensive experiments, where it improves trajectory prediction metrics by an average of 29.8% across all metrics compared to state-of-the-art baselines. These results highlight the benefit of using implicit, whole-body cues to achieve human-like navigation awareness on resourceconstrained platforms.  \nI. INTRODUCTION  \nSocial robot navigation is a critical area of robotics, driven by the growing demand for robots to operate autonomously in human-populated environments, from hospitals [1] to public spaces [2] . Unlike navigation in static or industrial domains, social navigation requires sensitivity to human behaviors and comfort [3] . To navigate fluently, a robot must go beyond collision avoidance and reason about others’ intended paths, such as differentiating a pedestrian continuing straight from one preparing to turn [4] . Human motor control studies suggest that pedestrians implicitly reveal navigational goals through body pose, head orientation, and gait [5] . For example, body pose can often indicate whether a person is walking or standing, and their intended walking direction.  \nHowever, despite the richness of these signals, many motion planning approaches oversimplify humans as lowdimensional 2D points or moving circles [6] . They typically react only to position and velocity using distance keeping [7] or proxemic rules [8], [9] . While efficient, such representations discard body language, making it difficult to perceive preparatory cues. As a result, pedestrian turns can appear more difficult to anticipate.  \n1 Ewha Womans University, [Korea.](Korea. songd@ewha.ac.kr)[ songd@ewha.ac.kr](Korea. songd@ewha.ac.kr);  \n2 George Mason University, USA. {nle47, mnazerir, apayande, [xiao](xiao}@gmu.ac.kr)[}](xiao}@gmu.ac.kr)[@gmu.ac.kr](xiao}@gmu.ac.kr); 3 University of Virginia, USA. {fyy2wsm, [rohanchandra](rohanchandra} @virginia.edu)[}](rohanchandra} @virginia.edu)[ @virginia.edu](rohanchandra} @virginia.edu); 4 Tufts University, USA. [reuth.mirsky@tufts.edu](reuth.mirsky@tufts.edu); 5 Semio, [USA.](USA. ross@semio.ai)[ ross@semio.ai](USA. ross@semio.ai); 6 Hokkaido University, [Japan.](Japan. ling@ist.hokudai.ac.jp)[ ling@ist.hokudai.ac.jp](Japan. ling@ist.hokudai.ac.jp)  \nFig. 1: HUMAIN distills a privileged knowledge from a teacher model into a student model. While the teacher uses these keypoints to plan robot trajectories, the student learns to infer social cues directly from RGB input to plan safe, socially compliant trajectories end-to-end.  \nWhile human trajectory prediction methods have leveraged pose to improve forecasting [10], [11], integrating these predictions into robot trajectory planning rema","cbCaiurRMmg4BqKu","https://ap.wps.com/l/cbCaiurRMmg4BqKu","pdf",9427528,1,"English","en",105,"# Introduction\n## Social navigation needs human behavior sensitivity\n## Limits of low-dimensional human motion representations\n## Teacher–student distillation for deployment efficiency","[{\"question\":\"What problem does HUMAIN address in social robot navigation?\",\"answer\":\"Social navigation requires adapting to human behavior and comfort, but many systems simplify humans into low-dimensional geometry and lose body-language cues needed for anticipating motion changes.\"},{\"question\":\"How does HUMAIN incorporate implicit social cues into robot planning?\",\"answer\":\"HUMAIN distills implicit social information into the planning loop by using a teacher model trained with privileged multimodal inputs (including skeletal keypoints) and transferring that knowledge to a lightweight student.\"},{\"question\":\"What training and deployment approach does HUMAIN use to be real-time capable?\",\"answer\":\"HUMAIN uses a two-stage teacher–student framework: the teacher learns rich latent representations from images and skeletal keypoints, then a student learns to match the teacher’s latent features and infer social dynamics using only raw images and a goal during 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