HumAIN: Human-Aware Implicit Social Robot Navigation

• URL: http://arxiv.org/abs/2607.07357v1
• Date: Wed, 08 Jul 2026 12:52:51 GMT
• Title: HumAIN: Human-Aware Implicit Social Robot Navigation
• Abstract summary: We present Human-Aware Implicit Social Robot Navigation (HumAIN)<n>HumAIN fuses implicit social cues directly into the planning loop via knowledge distillation.<n>We validate HumAIN through extensive experiments, where it improves trajectory prediction metrics by an average of 29.8%.
• Score: 15.91290186216647
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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