Max Robotics — Global Service Robot Directory + US Certification

About

Kaichun Mo is a researcher at the forefront of 3D computer vision and robotics, with a particular focus on articulated object manipulation, part-based assembly, and interactive simulation environments. His most influential contribution, SAPIEN (2020, 373 citations), established a landmark simulated environment enabling physically realistic interaction with articulated objects, becoming an essential benchmark for home-assistant robot research worldwide. Building on this foundation, Mo has made significant strides in teaching machines to understand and manipulate complex 3D structures, as demonstrated through his work on generative part assembly using dynamic graph learning and single-image-based 3D assembly inference. His research on manipulation affordance — including VAT-Mart and AdaAfford — advances robots' ability to interact intelligently with everyday articulated objects like cabinets and doors, even adapting from minimal interaction experiences. Mo has also explored human motion prediction through gaze-informed modeling and pioneered object-object affordance learning without costly annotations. His 2024 work on multi-part, multi-joint shape assembly reflects a continued evolution toward physically grounded robotic reasoning. Collectively, his publications have accumulated hundreds of citations, establishing him as a rising and impactful voice in embodied AI and robotic manipulation research.