A Learning-Based Framework for Safe Human-Robot Collaboration with Multiple Backup Control Barrier Functions

TL;DR

This paper introduces a learning-based control framework that uses multiple backup controllers and an LSTM classifier to ensure safety and respect driver intentions in human-robot collaboration, especially in complex environments.

Contribution

It proposes a novel switching scheme with an LSTM classifier to select among multiple backup controllers, reducing conservativeness and improving safety in human-robot interaction.

Findings

Successfully demonstrated on a dual-track robot in obstacle avoidance scenarios.

Guarantees safety while respecting driver intentions in complex environments.

Effectively reduces conservativeness compared to single backup controller approaches.

Abstract

Ensuring robot safety in complex environments is a difficult task due to actuation limits, such as torque bounds. This paper presents a safety-critical control framework that leverages learning-based switching between multiple backup controllers to formally guarantee safety under bounded control inputs while satisfying driver intention. By leveraging backup controllers designed to uphold safety and input constraints, backup control barrier functions (BCBFs) construct implicitly defined control invariance sets via a feasible quadratic program (QP). However, BCBF performance largely depends on the design and conservativeness of the chosen backup controller, especially in our setting of human-driven vehicles in complex, e.g, off-road, conditions. While conservativeness can be reduced by using multiple backup controllers, determining when to switch is an open problem. Consequently, we develop a broadcast scheme that estimates driver intention and integrates BCBFs with multiple backup strategies for human-robot interaction. An LSTM classifier uses data inputs from the robot, human, and safety algorithms to continually choose a backup controller in real-time. We demonstrate our method's efficacy on a dual-track robot in obstacle avoidance scenarios. Our framework guarantees robot safety while adhering to driver intention.