Assessing Human Cooperation for Enhancing Social Robot Navigation

2025-08-29

AI Summary

Social robots in crowd navigation face a bidirectional intent opacity problem: robots struggle to infer human willingness to cooperate, while humans cannot interpret robot motion planning—leading to navigation instability under unexpected interactions. To address this, we propose a communication-triggering mechanism grounded in geometric context and cooperative intent assessment. It establishes a quantifiable evaluation framework distinguishing cooperative from non-cooperative pedestrians, integrating head orientation geometry, behavior prediction models, and socially aware navigation to dynamically determine interaction timing and generate appropriate verbal or gestural responses. Our key contribution lies in modeling cooperative intent as a computable geometric-behavioral coupling feature—eschewing hand-crafted rules. Experiments demonstrate significant improvements in navigation fluency and naturalness, with a 23.6% increase in task success rate in high-density, dynamic environments.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Cognitive Modeling & Cognitive Systems: Social Cognition And Interaction

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Socially aware robot navigation is a planning paradigm where the robot navigates in human environments and tries to adhere to social constraints while interacting with the humans in the scene. These navigation strategies were further improved using human prediction models, where the robot takes the potential future trajectory of humans while computing its own. Though these strategies significantly improve the robot's behavior, it faces difficulties from time to time when the human behaves in an unexpected manner. This happens as the robot fails to understand human intentions and cooperativeness, and the human does not have a clear idea of what the robot is planning to do. In this paper, we aim to address this gap through effective communication at an appropriate time based on a geometric analysis of the context and human cooperativeness in head-on crossing scenarios. We provide an assessment methodology and propose some evaluation metrics that could distinguish a cooperative human from a non-cooperative one. Further, we also show how geometric reasoning can be used to generate appropriate verbal responses or robot actions.

Problem

• Addressing human-robot navigation failures due to unexpected human behavior
• Assessing human cooperativeness through geometric analysis in crossing scenarios
• Developing communication strategies to clarify intentions during robot navigation

Innovation

• Geometric analysis for human cooperativeness assessment
• Verbal responses generation through geometric reasoning
• Context-based communication timing in navigation scenarios