Projects

Publications

[C88] HOVER: Versatile Neural Whole-Body Controller for Humanoid Robots
Tairan He, Wenli Xiao, Toru Lin, Zhengyi Luo, Zhenjia Xu, Zhenyu Jiang, Changliu Liu, Guanya Shi, Xiaolong Wang, Linxi Fan and Yuke Zhu
IEEE International Conference on Robotics and Automation, 2025
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[C78] Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion
Tairan He, Chong Zhang, Wenli Xiao, Guanqi He, Changliu Liu and Guanya Shi
Robotics: Science and Systems, 2024
Outstanding Student Paper Award Finalist
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[C79] Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation
Tairan He, Zhengyi Luo, Wenli Xiao, Chong Zhang, Kris Kitani, Changliu Liu and Guanya Shi
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024
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[C82] OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning
Tairan He, Zhengyi Luo, Xialin He, Wenli Xiao, Chong Zhang, Weinan Zhang, Kris Kitani, Changliu Liu and Guanya Shi
Conference on Robot Learning, 2024
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[C54] Autocost: Evolving intrinsic cost for zero-violation reinforcement learning
Tairan He, Weiye Zhao and Changliu Liu
Proceedings of the AAAI Conference on Artificial Intelligence, 2023
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[C56] Safety index synthesis via sum-of-squares programming
Weiye Zhao, Tairan He, Tianhao Wei, Simin Liu and Changliu Liu
American Control Conference, 2023
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[C57] Probabilistic safeguard for reinforcement learning using safety index guided gaussian process models
Weiye Zhao, Tairan He and Changliu Liu
Learning for Dynamics and Control Conference, 2023
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[C60] State-wise safe reinforcement learning: A survey
Weiye Zhao, Tairan He, Rui Chen, Tianhao Wei and Changliu Liu
International Joint Conferences on Artificial Intelligence, 2023
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[C38] Model-free Safe Control for Zero-Violation Reinforcement Learning
Weiye Zhao, Tairan He and Changliu Liu
Conference on Robot Learning, 2021
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Abstract:

Maintaining safety under adaptation has long been considered to be an important capability for autonomous systems. As these systems estimate and change the ego-model of the system dynamics, questions regarding how to develop safety guarantees for such systems continue to be of interest. We propose a novel robust safe control methodology that uses set-based safety constraints to make a robotic system with dynamical uncertainties safely adapt and operate in its environment. The method consists of designing a scalar energy function (safety index) for an adaptive system with parametric uncertainty and an optimization-based approach for control synthesis. Simulation studies on a two-link manipulator are conducted and the results demonstrate the effectiveness of our proposed method in terms of generating provably safe control for adaptive systems with parametric uncertainty.