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

2025-10-19

IEEE/RJS International Conference on Intelligent RObots and Systems

AI Summary

This work addresses the safety challenges posed by complex human behaviors in human-robot interaction by proposing a novel approach that integrates Control Barrier Functions (CBFs) with conformal risk control. For the first time, conformal prediction error quantification is incorporated into the CBF framework to rigorously enforce probabilistic safety constraints. By dynamically adjusting safety boundaries and employing context-aware safety margins, the method significantly enhances safety while maintaining high task success rates. Experimental results in human-robot navigation scenarios demonstrate that the proposed approach substantially reduces collision rates and safety violations without compromising the efficiency of goal achievement.

Technology Category

• Intelligent Robots: Human-Robot Interaction
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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/.

Problem

• human-robot interaction
• probabilistic safety
• control barrier functions
• conformal risk control
• safety guarantees

Innovation

• Conformal Risk Control
• Control Barrier Functions
• Probabilistic Safety
• Human-Robot Interaction
• Dynamic Safety Margins