DiffCVaR: Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions
The framework integrates reinforcement learning with a differentiable CVaR barrier function to enable risk adaptation in crowded environments with uncertain obstacle motions. It jointly learns nominal control, risk level, and safety margin by modeling uncertainty using a Gaussian mixture model. This allows the system to achieve efficient navigation while explicitly enforcing probabilistic safety constraints through tractable optimization.