The human intention. A taxonomy attempt and its applications to robotics
J. E. Domínguez-Vidal, Alberto Sanfeliu
TL;DR
The paper addresses the lack of a universal definition for human intention in robotics and its impact on human–robot interaction. It proposes a psychology-informed taxonomy with five dichotomies (goal-oriented vs implementation; implicit vs explicit; conscious vs unconscious; individual vs collective; short-term vs long-term) and maps existing robotics work onto these axes. Through two illustrative use cases—collaborative search and collaborative object transport—it demonstrates how acknowledging the multifaceted nature of intention can improve interpretation, planning, and collaboration in HRI. The work highlights future challenges, including robots expressing intent, multi-agent intent reasoning, and negotiated roles, with broad implications for designing more transparent, reliable, and human-centered robotic systems.
Abstract
Despite a surge in robotics research dedicated to inferring and understanding human intent, a universally accepted definition remains elusive since existing works often equate human intention with specific task-related goals. This article seeks to address this gap by examining the multifaceted nature of intention. Drawing on insights from psychology, it attempts to consolidate a definition of intention into a comprehensible framework for a broader audience. The article classifies different types of intention based on psychological and communication studies, offering guidance to researchers shifting from pure technical enhancements to a more human-centric perspective in robotics. It then demonstrates how various robotics studies can be aligned with these intention categories. Finally, through in-depth analyses of collaborative search and object transport use cases, the article underscores the significance of considering the diverse facets of human intention.