Anticipated Risk-Guided Reinforcement Learning for Dynamic Obstacle Environments: Mei et al. propose a safe quadrotor flight framework

Mei et al. proposed an "anticipated risk-guided reinforcement learning" framework, enabling safe flight of high-speed quadrotors in dynamic cluttered environments by having the agent self-predict future collision risk maps. The method constructs directionally aligned future collision risk maps based on closest point of approach (CPA) physics, and adopts an asymmetric actor-critic architecture: during training, the critic is supervised by privileged simulator states, while at deployment, the actor predicts risk priors solely from onboard depth images.