Huaping Liu
Tsinghua University, Chinese Academy of Sciences, University Town of Shenzhen, Intelligent Health (United Kingdom), Center for Information Technology, East Asia School of Theology, National Engineering Research Center for Information Technology in Agriculture, PRG S&Tech (South Korea), National University of Defense Technology
Papers
143
Total Citations
3,814
H-Index
32
About
Huaping Liu is a prominent robotics and artificial intelligence researcher whose work spans robot perception, motion planning, soft robotics, and human-robot interaction. Best known for bridging sensory modalities in robotic systems, Liu has made landmark contributions to tactile sensing and object recognition, developing kernel sparse coding and extreme kernel sparse learning methods that enabled robots to identify objects through touch alone — work that has collectively garnered nearly 300 citations. His 2016 improved ant colony algorithm for robot path planning remains his most influential paper, with nearly 400 citations, reflecting his early and enduring impact on autonomous navigation. Liu's research on robotic grasp detection broke new ground by fusing visual and tactile sensing within a hybrid deep architecture, earning over 200 citations and helping define the multimodal approach now common in the field. His contributions extend to soft robotics — including a multimode grasping gripper using layer jamming and tendon-driven mechanisms — as well as human motion prediction through his TrajectoryCNN framework and gesture recognition via data gloves. His 2022 review of smart materials for soft actuators and sensors has rapidly accumulated over 125 citations, underscoring his influence across emerging robotics frontiers. With over 1,500 combined citations across his top works, Liu stands as a defining voice in intelligent robotic perception and embodied AI.
Research Focus
Key Achievements
Top Papers
- 1An improved ant colony algorithm for robot path planning399 citations · 2016
- 2A hybrid deep architecture for robotic grasp detection219 citations · 2017
- 3Object Recognition Using Tactile Measurements: Kernel Sparse Coding Methods188 citations · 2016
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- 53D human gesture capturing and recognition by the IMMU-based data glove120 citations · 2017
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- 9Extreme Kernel Sparse Learning for Tactile Object Recognition89 citations · 2016
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