Hello there! I am a first-year PHD student in HKU(the University of HongKong), advised by Prof. Ping Luo.
I am currently intern in Shanghai AI Lab, supervised by Dr. Jiangmiao Pang.
I obtained my Master and B.Eng. degree in Tsinghua University under the supervision of Prof Prof. Guijin Wang.
I am currently working on humanoid robots and reinforcement learning. If you are interested in my research or want to chat, please drop me an email.
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Junli Ren「任峻立」
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VB-Com: Learning Vision-Blind Composite Humanoid Locomotion Against Deficient Perception
Junli Ren, Tao Huang, Huayi Wang, Zirui Wang, Qingwei Ben, Jiangmiao Pang†, Ping Luo†
Preprint
[Project Page]
[Paper]
[Video]
[BibTeX]
We propose VB-Com, a composite framework that enables humanoid robots to determine when to rely on the vision policy and when to switch to the blind policy under perceptual deficiency.
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Learning Humanoid Standing-up Control across Diverse Postures
Tao Huang, Junli Ren, Huayi Wang, Zirui Wang, Qingwei Ben, Muning Wen, Xiao Chen, Jianan Li, Jiangmiao Pang†
Preprint
[Project Page]
[Paper]
[Video]
[BibTeX]
we present HoST (Humanoid Standing-up Control), a reinforcement learning framework that learns standing-up control from scratch, enabling robust sim-to-real transfer across diverse postures.
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BeamDojo: Learning Agile Humanoid Locomotion on Sparse Footholds
Huayi Wang, Zirui Wang, Junli Ren, Qingwei Ben, Tao Huang, Weinan Zhang, Jiangmiao Pang†
Preprint
[Project Page]
[Paper]
[Video]
[BibTeX]
BeamDojo achieves efficient learning in simulation and enables agile locomotion with precise foot placement on sparse footholds in the real world,
maintaining a high success rate even under significant external disturbances.
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Learning Humanoid Locomotion with Perceptive Internal Model
Junfeng Long*, Junli Ren*, Moji Shi*, Zirui Wang, Tao Huang, Ping Luo, Jiangmiao Pang†
International Conference on Robotics and Automation (ICRA), 2025
[Project Page]
[Paper]
[Code]
[BibTeX]
We propose the Perceptive Intenal Model (PIM), a method to estimate environmental disturbances
with perceptive information, enabling agile and robust locomotion for various humanoid robots on various terrains.
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TOP-Nav: Legged Navigation Integrating Terrain, Obstacle and Proprioception Estimation
Junli Ren*, Yikai Liu*, Yingru Dai, Junfeng Long, Guijin Wang†
Conference on Robot Learning (CoRL), 2024
[Project Page]
[Paper]
[Code]
[BibTeX]
We propose TOP-Nav, a novel legged navigation framework that integrates a comprehensive path planner
with Terrain awareness, Obstacle avoidance and close-loop Proprioception.
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