用风险控制方法让机器人与人互动时更安全，还能自动调节安全距离。
Safe Probabilistic Planning for Human-Robot Interaction using Conformal Risk Control
- 结合屏障函数与风险控制，动态调整安全边界。
- 实验显示碰撞率大幅降低，同时保持高成功率和高效控制。
- 适合需要高安全性的交互场景，如服务机器人、自动驾驶。
本文提出一种新型概率安全控制框架，用于人机交互场景。该方法将控制屏障函数（CBF）与一致风险控制（conformal risk control）相结合，量化并控制CBF安全值的预测误差，在考虑复杂人类行为的前提下，提供约束满足的概率性形式化保证。我们设计了一种算法，根据当前交互情境动态调整由一致风险控制生成的安全裕度。在人机导航实验中，与基线方法相比，该方法显著降低了碰撞率和安全违规次数，同时保持了较高的目标达成成功率和高效的控制性能。代码、仿真及补充材料可在项目网站获取：https://jakeagonzales.github.io/crc-cbf-website/。
原文摘要 · Abstract (English)
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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