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About

Tianying Ji is a rising researcher at the forefront of embodied intelligence and robotic manipulation, working to bridge the gap between physical interaction and cognitive computation. His work centers on enabling robots to achieve human-like dexterity and adaptive learning in real-world environments—a critical step toward Artificial General Intelligence (AGI). Ji’s most impactful contribution is his comprehensive survey on embodied intelligence (20 citations), which systematically maps the field’s advancements, challenges, and future directions, serving as an essential resource for researchers. He has also made significant strides in control theory, developing robust tube-based Model Predictive Control (MPC) methods that dramatically reduce computational delays—a key bottleneck in dexterous robot manipulation. His work on “Smooth Computation without Input Delay” (3 citations) and “Robust tube-based MPC” (5 citations) addresses the practical challenge of real-time planning, enabling smoother, more responsive robot motion. In reinforcement learning, Ji’s paper “Seizing Serendipity” (3 citations) introduces a novel off-policy actor-critic approach that exploits past successful experiences to improve Q-value learning, moving beyond conventional overestimation fixes. His research on robot cognitive learning (2 citations) further explores how robots can develop intelligence by understanding physical properties, mirroring human developmental processes. With a growing citation footprint and a focus on foundational problems, Ji is establishing himself as a key contributor to the next generation of intelligent, physically-capable robots.