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About

Lingfeng Tao is an emerging robotics researcher specializing in dexterous manipulation, multi-finger robotic control, and human-robot cooperation. His work sits at the intersection of deep reinforcement learning and real-world robotic systems, with a particular focus on solving the formidable challenge of in-hand manipulation using multi-finger robotic hands. Among his most notable contributions, Tao has developed innovative frameworks for tackling the high degrees of freedom inherent in dexterous manipulation. His multi-phase, multi-objective manipulation work (2022, 8 citations) introduced adaptive hierarchical curriculum learning to guide robots through tasks with shifting priorities — a significant step toward more generalizable robotic policies. Complementing this, his multi-agent approach for finger cooperation (2023, 5 citations) reframes each finger as an independent agent, enabling more flexible and structure-agnostic manipulation strategies. Tao has also advanced human-robot cooperation by addressing realistic scenarios where humans operate with only general, rather than specific, goals — a meaningful departure from overly constrained prior assumptions. His more recent telemanipulation work further bridges the gap between human dexterity and robotic execution. With a growing citation record and publications spanning 2021 to 2025, Tao represents a promising voice in next-generation intelligent robotics research.