# The human intention. A taxonomy attempt and its applications to robotics

arXiv: https://arxiv.org/abs/2602.15963 · HTML: https://mlsanity.com/paper/2602.15963/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2026.

## Built on by (most cited first, 4 of 4)

- [An Approach to Combining Video and Speech with Large Language Models in Human-Robot Interaction](https://mlsanity.com/paper/2602.20219/) (2026)
- [A ROS 2 Wrapper for Florence-2: Multi-Mode Local Vision-Language Inference for Robotic Systems](https://mlsanity.com/paper/2604.01179/) (2026)
- [Ablation Study of Multimodal Perception, Language Grounding, and Control for Human-Robot Interaction in an Object Detection and Grasping Task](https://mlsanity.com/paper/2605.00963/) (2026)
- [From Technical Metrics to User Perception: A User Study of a Multimodal Human-Robot Interaction System for Object Detection and Grasping](https://mlsanity.com/paper/2607.00530/) (2026)

## Abstract (arXiv)

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.