Max Robotics — Global Service Robot Directory + US Certification

About

Kangtong Mo is a rising researcher at the forefront of intelligent robotic control, specializing in the intersection of machine learning, nonlinear dynamics, and high-degree-of-freedom (DoF) manipulation. Mo’s core contributions center on developing self-adaptive, data-driven frameworks that overcome the limitations of traditional analytical models for complex robotic tasks. Their most cited work, “Invertible liquid neural network-based learning of inverse kinematics and dynamics for robotic manipulators” (2025, 12 citations), introduces a novel architecture that accurately estimates inverse kinematics and dynamics, capturing unmodeled nonlinearities without requiring compensatory controllers. This is complemented by “Self-Adaptive Robust Motion Planning for High DoF Robot Manipulator using Deep MPC” (2024, 11 citations), which leverages robust optimization algorithms for flexible, uncertainty-tolerant control. Mo has also pioneered the use of large language models for precision kinematic path optimization (2024, 8 citations), bridging semantic knowledge with real-world robotic execution. Additionally, their work on deep reinforcement learning for maximum solar energy tracking (2024, 3+ citations) demonstrates the transferability of high-DoF robotics to renewable energy systems. With a growing citation footprint and a focus on invertible neural networks and adaptive control, Mo is establishing a reputation for advancing robust, intelligent autonomy in robotics.