When the Inference Meets the Explicitness or Why Multimodality Can Make Us Forget About the Perfect Predictor
J. E. Dom\'inguez-Vidal, Alberto Sanfeliu

TL;DR
This study compares intention prediction and explicit communication systems in human-robot collaboration, revealing that naturalness and combined strategies improve human perception and task performance.
Contribution
It introduces and evaluates the integration of intention predictors and explicit communication methods in a human-robot task, highlighting their combined benefits.
Findings
Humans prefer natural communication systems despite higher failure rates.
Once performance is sufficient, humans no longer notice technical improvements.
The best results come from combining inference and explicit communication.
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…
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Taxonomy
TopicsSocial Robot Interaction and HRI · Action Observation and Synchronization · Human-Automation Interaction and Safety
