HumAIN: Human-Aware Implicit Social Robot Navigation

2026-07-08

AI Summary

This work addresses the challenge that existing social robots struggle to effectively perceive and respond to implicit human social cues such as posture and gait. To bridge this gap, the authors propose HumAIN, a novel framework that integrates full-body implicit social cues into the navigation planning loop for the first time. It employs knowledge distillation from a Transformer-based multimodal teacher model—which fuses historical images, skeletal keypoints, robot states, and goal information—to transfer human-aware representations to a lightweight student model. The approach jointly optimizes trajectory reconstruction and latent feature alignment, enabling efficient, real-time, socially compliant navigation on resource-constrained platforms. Experimental results demonstrate an average 29.8% improvement in trajectory prediction performance over state-of-the-art methods, underscoring the critical role of implicit social cues in human-like 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.

Problem

• social robot navigation
• human-aware navigation
• implicit social cues
• skeletal keypoints
• trajectory prediction

Innovation

• knowledge distillation
• implicit social cues
• social robot navigation
• transformer-based model
• trajectory prediction