Search Results for author: Tairan He

Found 11 papers, 4 papers with code

OmniH2O: Universal and Dexterous Human-to-Humanoid Whole-Body Teleoperation and Learning

no code implementations13 Jun 2024 Tairan He, Zhengyi Luo, Xialin He, Wenli Xiao, Chong Zhang, Weinan Zhang, Kris Kitani, Changliu Liu, Guanya Shi

We present OmniH2O (Omni Human-to-Humanoid), a learning-based system for whole-body humanoid teleoperation and autonomy.

Implicit Safe Set Algorithm for Provably Safe Reinforcement Learning

no code implementations4 May 2024 WeiYe Zhao, Tairan He, Feihan Li, Changliu Liu

Deep reinforcement learning (DRL) has demonstrated remarkable performance in many continuous control tasks.

Continuous Control reinforcement-learning +1

Learning Human-to-Humanoid Real-Time Whole-Body Teleoperation

no code implementations7 Mar 2024 Tairan He, Zhengyi Luo, Wenli Xiao, Chong Zhang, Kris Kitani, Changliu Liu, Guanya Shi

We present Human to Humanoid (H2O), a reinforcement learning (RL) based framework that enables real-time whole-body teleoperation of a full-sized humanoid robot with only an RGB camera.

Reinforcement Learning (RL)

Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion

1 code implementation31 Jan 2024 Tairan He, Chong Zhang, Wenli Xiao, Guanqi He, Changliu Liu, Guanya Shi

Legged robots navigating cluttered environments must be jointly agile for efficient task execution and safe to avoid collisions with obstacles or humans.

Safe Deep Policy Adaptation

1 code implementation8 Oct 2023 Wenli Xiao, Tairan He, John Dolan, Guanya Shi

In contrast, policy adaptation based on reinforcement learning (RL) offers versatility and generalizability but presents safety and robustness challenges.

reinforcement-learning Reinforcement Learning (RL) +1

State-wise Safe Reinforcement Learning: A Survey

no code implementations6 Feb 2023 WeiYe Zhao, Tairan He, Rui Chen, Tianhao Wei, Changliu Liu

Despite the tremendous success of Reinforcement Learning (RL) algorithms in simulation environments, applying RL to real-world applications still faces many challenges.

Autonomous Driving reinforcement-learning +3

Visual Imitation Learning with Patch Rewards

1 code implementation2 Feb 2023 Minghuan Liu, Tairan He, Weinan Zhang, Shuicheng Yan, Zhongwen Xu

Specifically, we present Adversarial Imitation Learning with Patch Rewards (PatchAIL), which employs a patch-based discriminator to measure the expertise of different local parts from given images and provide patch rewards.

Imitation Learning

AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement Learning

no code implementations24 Jan 2023 Tairan He, WeiYe Zhao, Changliu Liu

Results show that the converged policies with intrinsic costs in all environments achieve zero constraint violation and comparable performance with baselines.

reinforcement-learning Reinforcement Learning (RL)

AARL: Automated Auxiliary Loss for Reinforcement Learning

no code implementations29 Sep 2021 Tairan He, Yuge Zhang, Kan Ren, Che Wang, Weinan Zhang, Dongsheng Li, Yuqing Yang

A good state representation is crucial to reinforcement learning (RL) while an ideal representation is hard to learn only with signals from the RL objective.

reinforcement-learning Reinforcement Learning (RL)

Energy-Based Imitation Learning

1 code implementation20 Apr 2020 Minghuan Liu, Tairan He, Minkai Xu, Wei-Nan Zhang

We tackle a common scenario in imitation learning (IL), where agents try to recover the optimal policy from expert demonstrations without further access to the expert or environment reward signals.

Imitation Learning reinforcement-learning +1

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