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About

Xiangtong Yao is an emerging robotics and artificial intelligence researcher whose work sits at the intersection of lifelong learning, language-conditioned robot manipulation, and safety-critical control. His research addresses some of the most fundamental challenges in autonomous robotics: enabling robots to accumulate knowledge continuously without forgetting, interpret and execute complex natural language instructions, and operate safely in unpredictable real-world environments. Yao's most influential contribution, "Preserving and Combining Knowledge in Robotic Lifelong Reinforcement Learning" (29 citations), tackles the critical problem of catastrophic forgetting in autonomous systems, drawing inspiration from human cognitive development to advance general robotic intelligence. His complementary investigations into language-conditioned imitation learning and meta-reinforcement learning via language instructions (12 and 11 citations respectively) have helped establish a cohesive framework for robots that learn efficiently from minimal data while following human commands. His survey on language-conditioned robot manipulation further demonstrates his commitment to synthesizing knowledge across this rapidly evolving field. Beyond learning paradigms, Yao has made notable contributions to safety-critical control and energy-efficient neuromorphic computing, including LiDAR-based obstacle avoidance and spiking neural network lane-keeping systems. Together, his growing body of work reflects a comprehensive vision for intelligent, adaptive, and safe autonomous robots.