Safe Probabilistic Planning for Human-Robot Interaction using Conformal Risk Control

TL;DR

This paper introduces a probabilistic safe control framework for human-robot interaction that combines control barrier functions with conformal risk control to ensure safety guarantees and adapt to complex human behaviors.

Contribution

It presents a novel integration of conformal risk control with control barrier functions to provide formal safety guarantees in human-robot interaction scenarios.

Findings

Reduces collision rates and safety violations significantly.

Maintains high success rates in goal-reaching tasks.

Ensures formal safety guarantees during interaction.

Abstract

In this paper, we present a novel probabilistic safe control framework for human-robot interaction that combines control barrier functions (CBFs) with conformal risk control to provide formal safety guarantees while considering complex human behavior. The approach uses conformal risk control to quantify and control the prediction errors in CBF safety values and establishes formal guarantees on the probability of constraint satisfaction during interaction. We introduce an algorithm that dynamically adjusts the safety margins produced by conformal risk control based on the current interaction context. Through experiments on human-robot navigation scenarios, we demonstrate that our approach significantly reduces collision rates and safety violations as compared to baseline methods while maintaining high success rates in goal-reaching tasks and efficient control. The code, simulations, and other supplementary material can be found on the project website: https://jakeagonzales.github.io/crc-cbf-website/.