A survey of learning‐based robot motion planning

A fundamental task in robotics is to plan collision ‐ free motions among a set of obstacles. Recently, learning ‐ based motion ‐ planning methods have shown significant advantages in solving different planning problems in high ‐ dimensional spaces and complex environments. This article serves as a survey of various different learning ‐ based methods that have been applied to robot motion ‐ planning problems, including supervised, unsupervised learning, and reinforcement learning. These learning ‐ based methods either rely on a human ‐ crafted reward function for specific tasks or learn from successful planning experiences. The classical definition and learning ‐ related definition of motion ‐ planning problem are provided in this article. Different learning ‐ based motion ‐ planning algorithms are introduced, and the combination of classical motion ‐ planning and learning techniques is discussed in detail.