Hang Liu 「刘航」
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I am a first-year Master student at Umich focsing on Robot Learning and Legged Robot Control, advised by Prof. Maani Ghaffari.
Previously, I worked as a research assistant at Tsinghua Ai & Robot Lab and TSLab advised by Houde Liu and Linqi Ye. Besides, I joined a Startup Zerith and responsible for humanoid locomotion controller design.
My Research interests lies in Robot Learning especially in contact rich interaction, complex locomotion, mobile-manipulation.
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News
- [09/2024] One Paper was accepted by CORL 2024, See U in Munich!
- [06/2024] Two Paper was accepted by IROS 2024
- [05/2024] Participated in IEEE ICRA Quadruped Robot Challenge
- [10/2023] Win the China National Scholarship(top 0.2%)
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Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding
Hang Liu, Sangli Teng, Ben Liu, Wei Zhang, Maani Ghaffari
Under Review
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This paper introduces Discrete-time Hybrid Automata Learning (DHAL), a framework using on-policy Reinforcement Learning to identify and execute mode-switching without trajectory segmentation or event function learning. Hybrid dynamical systems, which include continuous flow and discrete mode switching, can model robotics tasks like legged robot locomotion. Model-based methods depend on predefined gaits, while model-free approaches lack explicit mode-switching knowledge. Current methods identify discrete modes via segmentation before regressing continuous flow, but learning high-dimensional complex rigid body dynamics without trajectory labels or segmentation is a challenging open problem. Our approach incorporates a beta policy distribution and a multi-critic architecture to model contact-guided motions, exemplified by a challenging quadrupedal robot skateboard task. We validate our method through simulations and real-world tests, demonstrating robust performance in hybrid dynamical systems.
@article{TBD
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Humanoid xxx Adaptive Risk-ware xxx for Robust Control
Junlong wu*, Yi Cheng*, Hang Liu, Houde Liu, Xueqian Wang, Bin Liang
Under Review
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Multi-Brain Collaborative Controll for Quadruped Robots
Hang Liu*, Yi Cheng*, Rankun Li, Xiaowen Hu, Linqi Ye, Houde Liu
CORL 2024
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In the field of locomotion task of quadruped robots, Blind Policy and Perceptive Policy each have their own advantages and limitations. The Blind Policy relies on preset sensor information and algorithms, suitable for known and structured environments, but it lacks adaptability in complex or unknown environments. The Perceptive Policy uses visual sensors to obtain detailed environmental information, allowing it to adapt to complex terrains, but its effectiveness is limited under occluded conditions, especially when perception fails. Unlike the Blind Policy, the Perceptive Policy is not as robust under these conditions. To address these challenges, we propose a Multi-Brain collaborative system that incorporates the concepts of Multi-Agent Reinforcement Learning and introduces collaboration between the Blind Policy and the Perceptive Policy. By applying this multi-policy collaborative model to a quadruped robot, the robot can maintain stable locomotion even when the perceptual system is impaired or observational data is incomplete. Our simulations and real-world experiments demonstrate that this system significantly improves the robot's passability and robustness against perception failures in complex environments, validating the effectiveness of multi-policy collaboration in enhancing robotic motion performance.
@article{TBD
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Quadruped robot traversing 3D complex environments
Yi Cheng*, Hang Liu*, Guoping Pan, LinQi Ye, Houde Liu, Bin Liang
IROS 2024(Oral Pitch)
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Traversing 3-D complex environments has always been a significant challenge for legged locomotion. Existing methods typically rely on external sensors such as vision and lidar to preemptively react to obstacles by acquiring environmental information. However, in scenarios like nighttime or dense forests, external sensors often fail to function properly, necessitating robots to rely on proprioceptive sensors to perceive diverse obstacles in the environment and respond promptly. This task is undeniably challenging. Our research finds that methods based on collision detection can enhance a robot's perception of environmental obstacles. In this work, we propose an end-to-end learning-based quadruped robot motion controller that relies solely on proprioceptive sensing. This controller can accurately detect, localize, and agilely respond to collisions in unknown and complex 3D environments, thereby improving the robot's traversability in complex environments. We demonstrate in both simulation and real-world experiments that our method enables quadruped robots to successfully traverse challenging obstacles in various complex environments.
@inproceedings{go2traverse,
title={Quadruped robot traversing 3D complex environments},
author={Yi Cheng, Hang Liu, Guoping Pan, Linqi Ye, Houde Liu, Bin Liang},
booktitle={arXiv preprint arXiv:2404.18225},
year={2024},
}
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Structural Optimization of Lightweight Bipedal Robot via SERL
Yi Cheng*, Chenxi Han*, Yuheng Min, LinQi Ye, Houde Liu, Hang Liu, Bin Liang
IROS 2024(Oral Pitch)
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Designing a bipedal robot is a complex and challenging task, especially when dealing with a multitude of structural parameters. Traditional design methods often rely on human intuition and experience. However, such approaches are time-consuming, labor-intensive, lack theoretical guidance, and struggle to obtain optimal design results within vast design spaces, thus failing to fully exploit the inherent performance potential of robots. In this context, this paper introduces the SERL (Structure Evolution Reinforcement Learning) algorithm, which combines reinforcement learning for locomotion tasks with evolution algorithms. The aim is to identify the optimal parameter combinations within a given multidimensional design space. Through the SERL algorithm, we successfully designed a bipedal robot named Wow Orin, where the optimal leg length is obtained through optimization based on body structure and motor torque. We have experimentally validated the effectiveness of the SERL algorithm, which is capable of optimizing the best structure within specified design space and task conditions. Additionally, to assess the performance gap between our designed robot and the current state-of-the-art robots, we comparedWow Orin with mainstream bipedal robots Cassie and Unitree H1. A series of experimental results demonstrate the Outstanding energy efficiency and performance of Wow Orin, further validating the feasibility of applying the SERL algorithm to practical design.
@inproceedings{TBD}
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