Can I read Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming on EtoBox?

Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming by Angelotti, Giorgio; Chanel, Caroline P. C.; Pinto, Adam H. M.; Lounis, Christophe; Chauffaut, Corentin; Drougard, Nicolas is a scholarly article available to read on EtoBox.

What is Offline Risk-sensitive RL with Partial Observability to Enhance Performance in Human-Robot Teaming about?

The integration of physiological computing into mixed-initiative human-robot interaction systems offers valuable advantages in autonomous task allocation by incorporating real-time features as human state observations into the decision-making system. This approach may alleviate the cognitive load on human operators by intelligently allocating mission tasks between agents. Nevertheless, accommodating a diverse pool of human participants with varying physiological and behavioral measurements presents a substantial challenge. To address this, resorting to a probabilistic framework becomes necessary, given the inherent uncertainty and partial observability on the human's state. Recent research suggests to learn a Partially Observable Markov Decision Process (POMDP) model from a data set of previously collected experiences that can be solved using Offline Reinforcement Learning (ORL) methods. In the present work, we not only highlight the potential of partially observable representations and physiological measurements to improve human operator state estimation and performance, but also enhance the overall mission effectiveness of a human-robot team. Importantly, as the fixed data set ma

Author

Angelotti, Giorgio; Chanel, Caroline P. C.; Pinto, Adam H. M.; Lounis, Christophe; Chauffaut, Corentin; Drougard, Nicolas

Published

2024

Language

EN