Robust Navigation

Robust navigation research aims to enable autonomous agents, from robots to UAVs, to navigate complex and unpredictable environments safely and efficiently. Current efforts focus on improving generalization across diverse scenarios using techniques like imitation learning, deep reinforcement learning with causal feature selection, and sensor fusion (e.g., LiDAR-GPS, visual-inertial, audio-visual). These advancements are crucial for deploying autonomous systems in real-world applications, such as assistive robotics, agriculture, and transportation, where robustness to unexpected events and sensor noise is paramount.