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About

Zongqing Lu is a leading researcher at the forefront of robotic dexterity and embodied artificial intelligence. His primary research areas encompass reinforcement learning, bimanual manipulation, and the integration of large language models (LLMs) with robotic systems. Lu's most significant contributions lie in advancing towards human-level dexterous manipulation, a notoriously difficult challenge in robotics due to the high degrees of freedom and complex coordination required. His seminal works, "Bi-DexHands" (2023) and its precursor (2022), have garnered over 68 combined citations, establishing a foundational framework for training robots to perform intricate, two-handed tasks through reinforcement learning. Beyond manipulation, Lu explores the intersection of perception and language, as demonstrated in "Steve-Eye" (2023), which equips LLM-based agents with visual capabilities for open-world interaction. He has also tackled practical challenges in autonomous systems, including underwater obstacle avoidance. Through these efforts, Lu is systematically bridging the gap between simulated learning and real-world robotic competence, making him a pivotal figure in the quest for versatile, intelligent machines.