Robust Obstacle Avoidance

Robust obstacle avoidance focuses on enabling autonomous agents, such as robots and vehicles, to safely navigate dynamic environments while efficiently reaching their goals. Current research emphasizes developing adaptive planning methods, often incorporating machine learning techniques like reinforcement learning and generative models (e.g., diffusion models), to handle unpredictable obstacle movements and limited sensing capabilities. These advancements leverage diverse sensor modalities (e.g., lidar, event cameras) and data-driven approaches (e.g., learning perception maps) to improve both the speed and robustness of obstacle avoidance, with significant implications for robotics, autonomous driving, and other fields requiring safe and efficient navigation.