Papers
9
Total Citations
225
H-Index
5
About
Faliang Chang is a leading researcher in robotic manipulation, computer vision, and human-robot interaction, with a focus on advancing autonomous grasping and surgical assistance systems. His major contributions include developing high-performance pixel-level grasp detection methods that address challenges in cluttered environments, such as the adaptive grasping and grasp-aware network, which has garnered 79 citations. In robot-assisted surgery, Chang pioneered real-time surgical instrument detection using convolutional neural network cascades and anchor-free architectures, achieving 53 and 52 citations respectively, significantly improving detection speed and accuracy for minimally invasive procedures. His work extends to non-prehensile manipulation through multi-stage reinforcement learning and bridging simulation-to-reality gaps in grasping, with recent papers on hierarchical diffusion policies for contact-guided trajectory generation. Chang’s research has been widely cited, with over 200 total citations, and his innovations in surgical tool detection and pixel-level grasping have practical implications for autonomous robotics and medical technology. Notable achievements include his contributions to explosive ordnance disposal robot control systems and human-robot interaction video understanding, demonstrating a broad impact across safety-critical and interactive domains.
Research Focus
Key Achievements
Top Papers
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- 4Multi-Stage Reinforcement Learning for Non-Prehensile Manipulation13 citations · 2024
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- 6Human–robot interaction-oriented video understanding of human actions5 citations · 2024
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