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

2026-02-21

AI Summary

This study addresses the challenge of real-time accuracy in human-robot collaborative carrying tasks, where reliance solely on intention prediction is vulnerable to the inherent randomness of human behavior. The work compares implicit intention prediction systems—based on force and velocity—with explicit communication modalities such as buttons and voice commands, and further investigates hybrid strategies that integrate both approaches. Using a custom social robot, IVO, equipped with multimodal perception—including force sensors, LiDAR, enhanced velocity prediction, speech recognition, and button interfaces—experiments reveal that users prefer explicit interaction methods that are natural and fault-tolerant. Moreover, once prediction performance surpasses a certain threshold, further improvements yield diminishing returns in user experience. The optimal collaboration paradigm emerges from a balanced integration of predictive and explicit methods, effectively harmonizing task efficiency with user satisfaction.

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

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• human-robot collaboration
• intention prediction
• explicit communication
• multimodality
• human intention

Innovation

• human-robot collaboration
• intention prediction
• explicit communication
• multimodal interaction
• user preference