Negotiating Control: Neurosymbolic Variable Autonomy

TL;DR

This paper explores neurosymbolic approaches to variable autonomy, aiming to improve real-time control balancing between humans and robots in unpredictable environments by integrating symbolic logic and reinforcement learning.

Contribution

It introduces a neurosymbolic framework that combines symbolic logic with reinforcement learning to enhance dynamic autonomy adjustments in robotic systems.

Findings

Symbolic logic improves context-aware autonomy adjustments.

Feedback from mixed-initiative control enhances safety.

Neurosymbolic methods outperform purely data-driven approaches.

Abstract

Variable autonomy equips a system, such as a robot, with mixed initiatives such that it can adjust its independence level based on the task's complexity and the surrounding environment. Variable autonomy solves two main problems in robotic planning: the first is the problem of humans being unable to keep focus in monitoring and intervening during robotic tasks without appropriate human factor indicators, and the second is achieving mission success in unforeseen and uncertain environments in the face of static reward structures. An open problem in variable autonomy is developing robust methods to dynamically balance autonomy and human intervention in real-time, ensuring optimal performance and safety in unpredictable and evolving environments. We posit that addressing unpredictable and evolving environments through an addition of rule-based symbolic logic has the potential to make autonomy adjustments more contextually reliable and adding feedback to reinforcement learning through data from mixed-initiative control further increases efficacy and safety of autonomous behaviour.