# Synthesis of Provably Correct Autonomy Protocols for Shared Control

## TL;DR

This paper presents a formal framework for synthesizing shared control protocols that ensure safety and performance in robotic systems, accounting for human behavior uncertainty, using probabilistic logic and inverse reinforcement learning.

## Contribution

It introduces a novel approach combining MDP modeling, inverse reinforcement learning, and quasiconvex programming to synthesize provably correct shared control strategies.

## Key findings

- Successfully applied to wheelchair navigation and UAV planning.
- Ensures safety and performance with probabilistic guarantees.
- Produces human-like behavior in control strategies.

## Abstract

We synthesize shared control protocols subject to probabilistic temporal logic specifications. More specifically, we develop a framework in which a human and an autonomy protocol can issue commands to carry out a certain task. We blend these commands into a joint input to a robot. We model the interaction between the human and the robot as a Markov decision process (MDP) that represents the shared control scenario. Using inverse reinforcement learning, we obtain an abstraction of the human's behavior and decisions. We use randomized strategies to account for randomness in human's decisions, caused by factors such as complexity of the task specifications or imperfect interfaces. We design the autonomy protocol to ensure that the resulting robot behavior satisfies given safety and performance specifications in probabilistic temporal logic. Additionally, the resulting strategies generate behavior as similar to the behavior induced by the human's commands as possible. We solve the underlying problem efficiently using quasiconvex programming. Case studies involving autonomous wheelchair navigation and unmanned aerial vehicle mission planning showcase the applicability of our approach.