Probabilistic Inference of Human Capabilities from Passive Observations
Peter Tisnikar, Gerard Canal, Matteo Leonetti
Abstract
Modern robots need to adapt to diverse human partners with whom they collaborate. To this end, learning a representation of human capabilities enables the robot to personalize their behaviour to their collaborators across multiple tasks. We propose CApability Modeling from Observations (CAMO), a model-based estimation algorithm, in which human capabilities that parameterize a given model are inferred from observations of the human behaviour on known collaborative tasks. We apply the method to joint limit learning in order to predict future trajectories of a 7-DOF manipulator arm. Furthermore, we show that CAMO can be used as a sub-task assignment routine in a simulated human–robot collaboration scenario, allowing the robot to adapt its task allocation to perform tasks that the person is not able to do.
BibTeX
@inproceedings{iros2024_probabilisticinf,
title = {Probabilistic Inference of Human Capabilities from Passive Observations},
author = {Peter Tisnikar and Gerard Canal and Matteo Leonetti},
booktitle = {IROS 2024},
year = {2024}
}