Chao Ni

Fast and Compute-Efficient Sampling-Based Local Exploration Planning via Distribution Learning

Exploration is a fundamental problem in robotics. While sampling-based planners have shown high performance and robustness, they are oftentimes compute intensive and can exhibit high variance. To this end, we propose to learn both components of sampling-based exploration. We present a method to dire