# Multimodal Uncertainty Reduction for Intention Recognition in Human-Robot Interaction

arXiv: https://arxiv.org/abs/1907.02426 · HTML: https://mlsanity.com/paper/1907.02426/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jul 2019.

## Built on by (most cited first, 11 of 11)

- [Multimodal Sense-Informed Prediction of 3D Human Motions](https://mlsanity.com/paper/2405.02911/) (2024)
- [Deep Learning Approaches for Multimodal Intent Recognition: A Survey](https://mlsanity.com/paper/2507.22934/) (2025)
- [Embodied AI with Foundation Models for Mobile Service Robots: A Systematic Review](https://mlsanity.com/paper/2505.20503/) (2025)
- [GRAIL: Goal Recognition Alignment through Imitation Learning](https://mlsanity.com/paper/2602.14252/) (2026)
- [General Dynamic Goal Recognition using Goal-Conditioned and Meta Reinforcement Learning](https://mlsanity.com/paper/2505.09737/) (2025)
- [Learning Multimodal AI Algorithms for Amplifying Limited User Input into High-dimensional Control Space](https://mlsanity.com/paper/2505.11366/) (2025)
- [Learning Multimodal Confidence for Intention Recognition in Human-Robot Interaction](https://mlsanity.com/paper/2405.14116/) (2024)
- [TransforMerger: Transformer-based Voice-Gesture Fusion for Robust Human-Robot Communication](https://mlsanity.com/paper/2504.01708/) (2025)
- [Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters](https://mlsanity.com/paper/2604.15339/) (2026)
- [IntentVLM: Open-Vocabulary Intention Recognition through Forward-Inverse Modeling with Video-Language Models](https://mlsanity.com/paper/2604.24002/) (2026)
- [Initiation of Interaction Detection Framework using a Nonverbal Cue for Human-Robot Interaction](https://mlsanity.com/paper/2605.10087/) (2026)

## Abstract (arXiv)

Assistive robots can potentially improve the quality of life and personal independence of elderly people by supporting everyday life activities. To guarantee a safe and intuitive interaction between human and robot, human intentions need to be recognized automatically. As humans communicate their intentions multimodally, the use of multiple modalities for intention recognition may not just increase the robustness against failure of individual modalities but especially reduce the uncertainty about the intention to be predicted. This is desirable as particularly in direct interaction between robots and potentially vulnerable humans a minimal uncertainty about the situation as well as knowledge about this actual uncertainty is necessary. Thus, in contrast to existing methods, in this work a new approach for multimodal intention recognition is introduced that focuses on uncertainty reduction through classifier fusion. For the four considered modalities speech, gestures, gaze directions and scene objects individual intention classifiers are trained, all of which output a probability distribution over all possible intentions. By combining these output distributions using the Bayesian method Independent Opinion Pool the uncertainty about the intention to be recognized can be decreased. The approach is evaluated in a collaborative human-robot interaction task with a 7-DoF robot arm. The results show that fused classifiers which combine multiple modalities outperform the respective individual base classifiers with respect to increased accuracy, robustness, and reduced uncertainty.