Publication

Perception–awareness–decision: Socially-aware robot navigation and interaction

Conference Article

Conference

ACM/IEEE International Conference on Human-Robot Interaction (HRI)

Edition

2026

Pages

258-262

Doc link

https://doi.org/10.1145/3776734.3794394

File

Download the digital copy of the doc pdf document

Abstract

Robots working in spaces shared by people need more than geometric mapping: they must recognize people, understand social context, and decide whether to proceed or negotiate passage. Traditional navigation pipelines lack this semantic understanding, often failing when progress depends on human cooperation. We introduce a Perception–Awareness–Decision (PAD) framework that systematically combines Simultaneous Localization and Mapping (SLAM) with Vision–Language Models (VLMs), speech recognition, and Large Language Models (LLMs), rather than simply stacking modules. PAD tries to emulate human perceptual organization by fusing multi-modal cues into a unified situational-awareness map capturing geometry, social context, and linguistic intent. This representation enables the decision layer to choose adaptively between safe replanning and context-appropriate verbal interaction. In a corridor-blocking task, PAD improves task success, increases safety margins, and produces behaviour that participants judged as more socially appropriate than a geometric baseline. These findings offer preliminary evidence that combining VLM-derived semantics with structured situational awareness can support more socially aware robot navigation.

Categories

automation.

Author keywords

Socially Aware Navigation, Dynamic Semantic Mapping, Foundation Models, Perception–Awareness–Decision

Scientific reference

E.J. Bejarano, V. Bo, A. Sanfeliu and A. Garrell Zulueta. Perception–awareness–decision: Socially-aware robot navigation and interaction, 2026 ACM/IEEE International Conference on Human-Robot Interaction, 2026, Edinburgh, Scotland, UK, pp. 258-262, ACM/IEEE.