Probabilistic Inference of Human Capabilities from Passive Observations

TY - CHAP

T1 - Probabilistic Inference of Human Capabilities from Passive Observations

AU - Tisnikar, Peter

AU - Canal, Gerard

AU - Leonetti, Matteo

PY - 2024/6/30

Y1 - 2024/6/30

N2 - 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.

AB - 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.

M3 - Conference paper

BT - 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

ER -