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About

Zhaoting Li is a robotics researcher whose work sits at the intersection of motion planning, autonomous exploration, and intelligent perception. Li’s most influential contribution is the widely-cited survey “A survey of learning‐based robot motion planning” (2021, 93 citations), which comprehensively maps how machine learning—particularly deep and reinforcement learning—is transforming the classical challenge of collision-free path planning in high-dimensional, cluttered spaces. Building on this foundation, Li has developed novel methods that use recurrent generative models to efficiently generate heuristics for path planning, dramatically reducing the computational cost of searching for optimal trajectories even in 2D environments. In the domain of autonomous exploration, Li has pioneered learning-based strategies that leverage 4D point-cloud-like observations to guide a robot’s frontier selection, enabling more intelligent and efficient exploration of unknown environments. Li has also made notable contributions to industrial automation, designing an automatic laser scanning system that uses Mask R-CNN to plan scanning trajectories for objects with unknown models—a practical solution for reverse engineering and quality control. With a growing citation record and a focus on bridging learning algorithms with real-world robotic systems, Li is a rising voice in the push toward fully autonomous, perceptive robots.