Publication
Inference vs. explicitness. Do we really need the perfect predictor? The human-robot collaborative object transportation case
Conference Article
Conference
IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
Edition
32nd
Pages
1866-1871
Doc link
https://doi.org/10.1109/RO-MAN57019.2023.10309648
File
Abstract
When robots interact with humans, limitations in their internal models arise due to the uncertainty and even randomness of human behavior. This has led to attempts to predict human future actions and infer their intent. However, some authors argue for combining inference engines with communication systems that explicitly elicit human intention. This work builds on our Perception-Intention-Action (PIA) cycle, a framework that considers human intention at the same level as perception of the environment. The PIA cycle is used in a collaborative task to compare the effect on different human- robot interaction aspects of using a force predictor that infers human implicit intention versus a communication system that explicitly elicits human intention. A study with 18 volunteers shows that allowing humans to directly express themselves can achieve the same improvement as an intention predictor.
Categories
automation.
Author keywords
Physical Human-Robot Interaction, Intent Detection, Human-in-the-Loop
Scientific reference
J.E. Domínguez and A. Sanfeliu. Inference vs. explicitness. Do we really need the perfect predictor? The human-robot collaborative object transportation case, 32nd IEEE International Symposium on Robot and Human Interactive Communication, 2023, Busan, Korea, pp. 1866-1871.
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