Robotic Contextual Awareness for Human-Robot Collaboration and Environmental Understanding

2026-07-11

AI Summary

This work addresses the challenge of enabling autonomous mobile robots to safely and naturally collaborate with humans in dynamic environments, where limited contextual awareness often hinders effective interaction. To this end, the authors propose a dual-path perception framework that integrates person re-identification with multi-object tracking to achieve persistent and accurate identification of specific individuals. Concurrently, the framework unifies geometric mapping and semantic scene understanding to construct a coherent environmental context representation. By synergistically combining identity-aware tracking with rich scene semantics, the approach substantially enhances the robot’s situational awareness, navigation safety, and naturalness of human–robot interaction in complex, dynamic settings, thereby offering a scalable perceptual foundation for embodied intelligent systems.

Abstract

The transition of autonomous mobile robots from controlled industrial settings to dynamic, human-centric environments, such as manufacturing, logistics, and healthcare, has made their safe and autonomous operation a critical area of research. These sophisticated machines must be capable of perceiving, understanding, and interacting with their surroundings to navigate freely and perform complex tasks. A significant obstacle to achieving this is the lack of comprehensive contextual awareness, which requires a robot to recognize its spatial environment and identify the objects and actors within it. Without this perceptual knowledge, robots struggle to plan adaptive behaviors or engage in meaningful interaction with humans. This thesis presents novel solutions to this challenge by exploring two distinct but complementary research directions. The first direction involves human re-identification and tracking to improve Human-Robot Collaboration. Our developed approach enables a mobile robot to recognize a specific person, facilitating targeted collaboration while ignoring other individuals. The second direction focuses on enhancing the robot's overall perceptual capabilities to understand its environment geometrically and semantically. Geometric information is vital for motion planning and collision avoidance, while semantic knowledge provides the robot with a richer understanding for more advanced interaction. Both solutions are driven by the improvement of the semantical understanding of robots that enhance their knowledge of their surroundings, allowing a smoother and more natural interaction between robots, humans, and the environment. The contributions of this work in human re-identification and environmental understanding represent a significant step toward a future where robots are more contextually aware, enabling safer coexistence and more effective collaboration.

Problem

• contextual awareness
• human-robot collaboration
• environmental understanding
• autonomous mobile robots
• semantic perception

Innovation

• contextual awareness
• human re-identification
• semantic understanding
• geometric perception
• human-robot collaboration