Max Robotics — Global Service Robot Directory + US Certification

About

Yiheng Han is a robotics researcher whose work bridges deep reinforcement learning, motion planning, and agricultural automation. His most impactful contribution is a novel deep reinforcement learning framework for robot collision avoidance that integrates self-state-attention mechanisms with sensor fusion from 3D LiDAR, enabling safer navigation in complex environments—a paper that has garnered 59 citations. Han also leads innovation in agricultural robotics with the AHPPEBot, an autonomous tomato harvesting robot that leverages crop phenotyping and pose estimation to improve harvesting success rates while minimizing crop damage. His technical contributions extend to active object reconstruction, where he developed a double-branch next-best-view network for efficient 3D scanning, and to configuration space decomposition methods that accelerate learning-based collision checking for high-degree-of-freedom robots. Han’s work on SE(3) reachability map generation using interplanar convolutions addresses computational bottlenecks in industrial robotics. With a career spanning from early work on human-computer boxing game motion planning to cutting-edge deep learning for robotics, Han demonstrates a sustained commitment to making robots safer, more autonomous, and more capable in real-world tasks.