Papers
6
Total Citations
123
H-Index
4
About
Hailin Ren is a robotics researcher whose work bridges machine learning, sensor fusion, and autonomous navigation. His primary research areas include inverse kinematics, reinforcement learning for robot navigation, and heterogeneous sensor fusion. Ren’s most impactful contribution is his 2019 paper on learning inverse kinematics and dynamics of robotic manipulators using generative adversarial networks, which has garnered 96 citations—a testament to its influence in advancing data-driven robotics. He also developed a neural network-based heterogeneous sensor fusion approach for real-time traversability estimation in mobile robots, addressing the challenge of reliable navigation in unstructured terrain. His work on reinforcement learning for robot path planning in nondeterministic environments and deep reinforcement learning for obstacle avoidance further underscores his focus on autonomous systems operating under uncertainty. Notably, Ren has explored dynamic stabilization through a variable inertia spatial robotic tail for bipedal platforms, showcasing his versatility. More recently, he has contributed to bibliometric analyses of robotic-assisted surgery. With a total of over 120 citations across his key publications, Ren’s research is shaping the future of intelligent, adaptive robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2Neural Network Based Heterogeneous Sensor Fusion for Robot Motion Planning10 citations · 2019
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- 4Mobile Robot Obstacle Avoidance Based on Deep Reinforcement Learning4 citations · 2019
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