# When the Inference Meets the Explicitness or Why Multimodality Can Make Us Forget About the Perfect Predictor

arXiv: https://arxiv.org/abs/2602.18850 · HTML: https://mlsanity.com/paper/2602.18850/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 62 references that are in the map.
- First posted on arXiv 21 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Broadcasted Residual Learning for Efficient Keyword Spotting](https://mlsanity.com/paper/2106.04140/) (2021)
- [MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition](https://mlsanity.com/paper/2004.08531/) (2020)
- [On the Utility of Model Learning in HRI](https://mlsanity.com/paper/1901.01291/) (2019)
- [How to select and use tools? : Active Perception of Target Objects Using Multimodal Deep Learning](https://mlsanity.com/paper/2106.02445/) (2021)
- [Anticipation in Human-Robot Cooperation: A Recurrent Neural Network Approach for Multiple Action Sequences Prediction](https://mlsanity.com/paper/1802.10503/) (2018)
- [Robot Planning with Mathematical Models of Human State and Action](https://mlsanity.com/paper/1705.04226/) (2017)
- [Communicating Inferred Goals with Passive Augmented Reality and Active Haptic Feedback](https://mlsanity.com/paper/2109.01747/) (2021)

## 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)

Although in the literature it is common to find predictors and inference systems that try to predict human intentions, the uncertainty of these models due to the randomness of human behavior has led some authors to start advocating the use of communication systems that explicitly elicit human intention. In this work, it is analyzed the use of four different communication systems with a human-robot collaborative object transportation task as experimental testbed: two intention predictors (one based on force prediction and another with an enhanced velocity prediction algorithm) and two explicit communication methods (a button interface and a voice-command recognition system). These systems were integrated into IVO, a custom mobile social robot equipped with force sensor to detect the force exchange between both agents and LiDAR to detect the environment. The collaborative task required transporting an object over a 5-7 meter distance with obstacles in the middle, demanding rapid decisions and precise physical coordination. 75 volunteers perform a total of 255 executions divided into three groups, testing inference systems in the first round, communication systems in the second, and the combined strategies in the third. The results show that, 1) once sufficient performance is achieved, the human no longer notices and positively assesses technical improvements; 2) the human prefers systems that are more natural to them even though they have higher failure rates; and 3) the preferred option is the right combination of both systems.