Max Robotics — Global Service Robot Directory + US Certification

About

Tingfan Wu is a robotics researcher whose work spans humanoid robot control, biomechanical locomotion, tactile perception, and machine learning for robotic systems. He is perhaps best known for his foundational contributions to whole-body control of humanoid robots, most notably through a momentum-based quadratic programming framework applied to Boston Dynamics' Atlas robot — a paper that has garnered over 255 citations and remains a cornerstone reference in legged robotics. His involvement with Team IHMC during the DARPA Robotics Challenge further cemented his reputation, with two retrospective publications collectively drawing nearly 300 citations and documenting hard-won lessons from one of robotics' most demanding real-world competitions, in which the team placed first or second across all three phases. Wu's research also explores energy-efficient bipedal locomotion, particularly through parallel elastic elements in the STEPPR robot, and extends into system identification using semi-parametric Gaussian processes and pneumatic actuator modeling. More recently, he has pushed into cutting-edge visuotactile perception, with his NeuralFeels work combining neural fields and multimodal sensing to enable dexterous in-hand manipulation — already accumulating 66 citations since 2024. Earlier work on robotic facial expression learning reflects his longstanding interest in human-robot interaction. Across disciplines, Wu's research consistently bridges theoretical rigor with impactful real-world application.