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About

Daheng Li is a leading researcher in dexterous robotic manipulation, focusing on enabling anthropomorphic hands to grasp and hand over objects with human-like skill and adaptability. Their major contributions center on deep learning-based grasp generation and human-robot interaction, particularly for multi-fingered hands—a domain far more complex than traditional parallel-jaw grippers. Li’s most cited work, "DVGG: Deep Variational Grasp Generation for Dextrous Manipulation" (48 citations), introduces a variational network that models intricate hand-object interactions for efficient, stable grasping. They have also pioneered human-to-robot dexterous handovers, with papers like "Learning Human-to-Robot Dexterous Handovers for Anthropomorphic Hand" (25 citations) and a comprehensive survey (21 citations) that charts the field’s progress and future directions. Notably, Li’s research on functional grasping from few demonstrations (21 citations) and grasping in clutter (15 citations) addresses key challenges in generalization and real-world deployment. Their work has accumulated over 145 citations, demonstrating significant impact in advancing robotic hands from lab curiosities to practical collaborators. Li’s achievements include developing reactive handover systems and collaborative tele-grasping methods, pushing the boundaries of how robots can seamlessly work alongside humans in dynamic environments.