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」

Selected Publications
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.

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.

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.

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.

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.


Updated at Feb. 2025.
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