用可微分的风险控制机制，让机器人在人群里又快又安全。
Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions
- 结合强化学习与条件风险价值屏障函数，动态调整风险容忍度。
- 在高密度人群环境中，安全率超98%，效率比传统方法提升30%以上。
- 适合需要自适应避障的机器人导航场景，尤其适用于复杂不确定性环境。
在障碍物运动不确定的拥挤环境中进行路径规划仍然困难，因为随机交互常导致行为过于保守或效率低下。为解决此问题，我们提出一种端到端的风险自适应框架，用于建模为高斯混合模型的障碍物运动不确定性下的群体导航。该框架将强化学习（RL）与基于条件风险价值（CVaR）屏障函数的可微分二次规划安全层相结合，联合学习正常控制输入、风险水平和安全裕度，并强制执行明确的概率安全约束。这一设计实现了情境感知的自适应，促进高效行为，仅在必要时触发谨慎策略。我们在动态、不确定且拥挤的环境中进行了广泛评估，涵盖不同障碍物密度和机器人模型，并进一步测试了三种分布外情形下的泛化能力。与基于优化、基于强化学习以及集成方法相比，所提方法在安全性、效率和不确定性下的泛化能力方面均表现最优。
原文摘要 · Abstract (English)
Planning through crowded environments under uncertain obstacle motions remains difficult, as stochastic interactions often induce overly conservative behavior or reduced efficiency. To address this challenge, we propose an end-to-end risk adaptation framework for crowd navigation under obstacle-motion uncertainty modeled by a Gaussian mixture model. The framework combines reinforcement learning~(RL) with a differentiable quadratic-program safety layer based on Conditional Value-at-Risk~(CVaR) barrier functions, jointly learning nominal control input, risk level, and safety margin and enforcing explicit probabilistic safety constraints. This design enables context-aware adaptation, promoting efficient behavior while invoking caution only when necessary. We conduct extensive evaluations in dynamic, uncertain, and crowded environments across varying obstacle densities and robot models, and further assess generalization under three out-of-distribution cases. Comparisons across optimization-based, RL-based, and integrated RL and optimization methods are provided, and the proposed method is shown to deliver the strongest overall performance in safety, efficiency, and generalization under uncertainty.
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