Hello there! I am a visiting scholar at Hybrid Robotics, UC Berkeley advised by Prof. Koushil Sreenath. Previously, I worked on Legged Robot Locomotion with Dr. Jiangmiao Pang and Distributed Systems with Prof. Youlong Wu. I received my Bachelor's degree in Computer Science from ShanghaiTech University.

I am interested in enabling robotic systems to achieve functionality and safety. Toward this goal, I focus on: (a) bridging control and machine learning to develop methods possessing their advantages; (b) training and application of dynamics models (world models); (c) developing robot foundation model for general purpose robots.

I am actively applying for Ph.D. position starting from Fall 2025. If you are interested in my research or want to chat, please drop me an email.

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Junfeng Long「龙俊峰」

Preprints
GRUtopia: Dream General Robots in a City at Scale

Hanqing Wang*, Jiahe Chen*, Wensi Huang*, Qingwei Ben*, Tai Wang*, Boyu Mi*, Tao Huang, Siheng Zhao, Yilun Chen, Sizhe Yang, Peizhou Cao, Wenye Yu, Zichao Ye, Jialun Li, Junfeng Long, Zirui Wang, Huiling Wang, Ying Zhao, Zhongying Tu, Yu Qiao, Dahua Lin, Jiangmiao Pang†

Under Review, 2024

[Project Page] [Paper] [Code] [BibTeX]

We proposed GRUtopia, the first simulated interactive 3D society designed for various robots. It features (a)GRScenes, a dataset with 100k interactive and finely annotated scenes. (b) GRResidents, a LLM driven NPC system. (c) GRBench, a benchmark posing moderately challenging tasks.

Publications
Learning Humanoid Locomotion with Perceptive Internal Model

Junfeng Long*, Junli Ren*, Moji Shi*, Zirui Wang, Tao Huang, Ping Luo, Jiangmiao Pang†

LocoLearn Workshop at Conference on Robot Learning (CoRL), 2024
Submitted to 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.

Parallelizing Model-based Reinforcement Learning Over the Sequence Length

Zirui Wang, Yue Deng, Junfeng Long, Yin Zhang†

Annual Conference on Neural Information Processing Systems (NeurIPS), 2024

[Paper] [Code] [BibTeX]

We propose the Parallelized Model-based Reinforcement Learning (PaMoRL) framework. PaMoRL introduces two novel techniques: the Parallel World Model (PWM) and the Parallelized Eligibility Trace Estimation (PETE) to parallelize both model and policy learning stages of current MBRL methods over the sequence length.

Learning H-infinity Locomotion Control

Junfeng Long*, Wenye Yu*, Quanyi Li*, Zirui Wang, Dahua Lin, Jiangmiao Pang†

Conference on Robot Learning (CoRL) & LocoLearn Workshop (Best Poster Award), 2024

[Project Page] [Paper] [Code] [BibTeX]

We present the H-infinity Locomotion Control, an adversarial framework improving the control policy's ability to resist external disturbances with H-infinity performance guarantee.

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.

Hybrid Internal Model: Learning Agile Legged Locomotion with Simulated Robot Response

Junfeng Long*, Zirui Wang*, Quanyi Li, Jiawei Gao, Liu Cao, Jiangmiao Pang†

International Conference on Learning Representations (ICLR), 2024

[Project Page] [Paper] [Code] [BibTeX]

We present the Hybrid Internal Model, a method enabling the control policy to estimate environmental disturbances by only explicitly estimating velocity and implicitly simulating the system's response.

teaser
On the Optimality of Data Exchange for Master-Aided Edge Computing Systems

Haoning Chen, Junfeng Long, Shuai Ma, Mingjian Tang, Youlong Wu

IEEE Transactions on Communications (TCOM), 2023

[Paper] [BibTeX]

We propose a coded scheme to reduce the communication latency by exploiting computation and communication capabilities of all nodes and creating coded multicast opportunities.More importantly, we prove that the proposed scheme is always optimal, i.e., achieving the minimum communication latency, for arbitrary computing and storage abilities at the master.

Awards
Best Poster Award of LocoLearn Workshop at CoRL 2024

Outstanding Teaching Assistant Award, ShanghaiTech University, 2021

Outstanding Individual in Industry Practice, ShanghaiTech University, 2021


Updated at November 2024.
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