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This paper introduces a probabilistic safe control framework for human-robot interaction that integrates control barrier functions (CBFs) with conformal risk control to ensure safety while accounting for complex human behavior. The approach quantifies and controls prediction errors in CBF safety values using conformal risk control, providing formal guarantees on the probability of constraint satisfaction. Experimental results in human-robot navigation scenarios show a significant reduction in collision rates and safety violations compared to baselines, while maintaining high success rates in goal-reaching tasks.
Guarantee safe human-robot interaction by dynamically adjusting safety margins based on real-time context, slashing collision rates compared to standard control barrier function methods.
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/.
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