{"ID":6138234,"CreatedAt":"2026-07-09T01:07:32.349475501Z","UpdatedAt":"2026-07-11T12:42:53.627509628Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.07357","arxiv_id":"2607.07357","title":"HumAIN: Human-Aware Implicit Social Robot Navigation","abstract":"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's target 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 resource-constrained platforms.","short_abstract":"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....","url_abs":"https://arxiv.org/abs/2607.07357","url_pdf":"https://arxiv.org/pdf/2607.07357v1","authors":"[\"Daeun Song\",\"Nhat Le\",\"Jeffrey Chen\",\"Mohammad Nazeri\",\"Amirreza Payandeh\",\"Rohan Chandra\",\"Reuth Mirsky\",\"Ross Mead\",\"Ling Xiao\",\"Xuesu Xiao\"]","published":"2026-07-08T12:52:51Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false}