Shared Autonomy Systems with Stochastic Operator Models

Shared Autonomy Systems with Stochastic Operator Models

Clarissa Costen, Marc Rigter, Bruno Lacerda, Nick Hawes

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Main Track. Pages 4614-4620. https://doi.org/10.24963/ijcai.2022/640

We consider shared autonomy systems where multiple operators (AI and human), can interact with the environment, e.g. by controlling a robot. The decision problem for the shared autonomy system is to select which operator takes control at each timestep, such that a reward specifying the intended system behaviour is maximised. The performance of the human operator is influenced by unobserved factors, such as fatigue or skill level. Therefore, the system must reason over stochastic models of operator performance. We present a framework for stochastic operators in shared autonomy systems (SO-SAS), where we represent operators using rich, partially observable models. We formalise SO-SAS as a mixed-observability Markov decision process, where environment states are fully observable and internal operator states are hidden. We test SO-SAS on a simulated domain and a computer game, empirically showing it results in better performance compared to traditional formulations of shared autonomy systems.
Keywords:
Planning and Scheduling: Planning under Uncertainty
Agent-based and Multi-agent Systems: Human-Agent Interaction
Humans and AI: Human-AI Collaboration
Planning and Scheduling: Markov Decisions Processes
Robotics: Human Robot Interaction