Max Robotics — Global Service Robot Directory + US Certification

About

Haonan He is a rising researcher at the intersection of robotics, deep learning, and physics-based optimization. His work focuses on bridging the gap between data-driven perception and model-based control, aiming to create robotic systems that are both adaptable and reliable. He is best known as a core contributor to **PyPose**, a groundbreaking open-source library for robot learning that seamlessly integrates deep learning with physics-based optimization. This library, detailed in his highly cited 2023 paper (35 citations), addresses a critical challenge: while deep learning excels at perception, it often fails to generalize; conversely, physics-based methods are robust but struggle with complex tasks. PyPose provides a unified framework to combine their strengths, enabling more capable and generalizable robotic systems. More recently, He has advanced this vision with **iKap** (2025), a kinematics-aware planning framework that uses imperative learning to generate collision-free trajectories directly from visual input. With a growing citation impact and contributions that are shaping the next generation of robot learning tools, Haonan He is a key figure to watch in the push toward truly intelligent, autonomous robots.