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About

Jie Lai is a leading researcher in robotics and wireless sensing, whose work bridges the gap between autonomous systems and sustainable energy solutions. His primary research areas include wheel-legged robot balance control, adaptive optimal control, and self-powered wireless sensing technologies. Lai’s major contributions lie in developing innovative control strategies for wheel-bipedal robots, such as the novel Ollie platform, where he pioneered learning-based and data-driven approaches using reinforcement learning and adaptive dynamic programming to achieve robust balance without accurate dynamic models. His most cited work, “A Paradigm Shift Fully Self-Powered Long-Distance Wireless Sensing Solution” (118 citations), introduces a groundbreaking discharge-induced displacement current method that eliminates the need for bulky, rigid power modules in IoT devices. With over 400 total citations across his top papers, Lai’s impact is evident in both robotics and wireless sensing communities. Notably, his balance control techniques for wheel-legged robots have been recognized for enabling agile, stepping motions without roll joints, advancing the capabilities of platforms like Boston Dynamics’ Handle. Lai’s research continues to inspire new directions in adaptive robotics and energy-autonomous sensing systems.