Projects

Publications

2025

[C97] ASAP: Aligning Simulation and Real-World Physics for Learning Agile Humanoid Whole-Body Skills
Tairan He, Jiawei Gao, Wenli Xiao, Yuanhang Zhang, Zi Wang, Jiashun Wang, Zhengyi Luo, Guanqi He, Nikhil Sobanbab, Chaoyi Pan and others others
Robotics: Science and Systems, 2025
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Abstract:

Humanoid robots hold the potential for unparalleled versatility in performing human-like, whole-body skills. However, achieving agile and coordinated whole-body motions remains a significant challenge due to the dynamics mismatch between simulation and the real world. Existing approaches, such as system identification (SysID) and domain randomization (DR) methods, often rely on labor-intensive parameter tuning or result in overly conservative policies that sacrifice agility. In this paper, we present ASAP (Aligning Simulation and Real-World Physics), a two-stage framework designed to tackle the dynamics mismatch and enable agile humanoid whole-body skills. In the first stage, we pre-train motion tracking policies in simulation using retargeted human motion data. In the second stage, we deploy the policies in the real world and collect real-world data to train a delta (residual) action model that compensates for the dynamics mismatch. Then, ASAP fine-tunes pre-trained policies with the delta action model integrated into the simulator to align effectively with real-world dynamics. We evaluate ASAP across three transfer scenarios: IsaacGym to IsaacSim, IsaacGym to Genesis, and IsaacGym to the real-world Unitree G1 humanoid robot. Our approach significantly improves agility and whole-body coordination across various dynamic motions, reducing tracking error compared to SysID, DR, and delta dynamics learning baselines. ASAP enables highly agile motions that were previously difficult to achieve, demonstrating the potential of delta action learning in bridging simulation and real-world dynamics. These results suggest a promising sim-to-real direction for developing more expressive and agile humanoids.
[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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2024

[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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[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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[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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2023

[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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[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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[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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[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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2021

[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.