Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints

TL;DR

This paper introduces a novel shared control method that guarantees safety and minimal user deviation even with complex non-convex constraints, validated through extensive user studies and real-world robotic experiments.

Contribution

It proposes a Constraint-Aware Assistive Controller using Robust Controlled Invariant Sets and Mixed-Integer Quadratic Programming to ensure safety and feasibility under non-convex constraints.

Findings

Improved safety and user trust in shared control systems.

Enhanced task performance with minimal user intervention.

Validated effectiveness through large-scale user study and robotic experiments.

Abstract

Shared control combines human intention with autonomous decision-making. At the low level, the primary goal is to maintain safety regardless of the user's input to the system. However, existing shared control methods-based on, e.g., Model Predictive Control, Control Barrier Functions, or learning-based control-often face challenges with feasibility, scalability, and mixed constraints. To address these challenges, we propose a Constraint-Aware Assistive Controller that computes control actions online while ensuring recursive feasibility, strict constraint satisfaction, and minimal deviation from the user's intent. It also accommodates a structured class of non-convex constraints common in real-world settings. We leverage Robust Controlled Invariant Sets for recursive feasibility and a Mixed-Integer Quadratic Programming formulation to handle non-convex constraints. We validate the approach through a large-scale user study with 66 participants-one of the most extensive in shared control research-using a simulated environment to assess task load, trust, and perceived control, in addition to performance. The results show consistent improvements across all these aspects without compromising safety and user intent. Additionally, a real-world experiment on a robotic manipulator demonstrates the framework's applicability under bounded disturbances, ensuring safety and collision-free operation.