Search Results for author: Jie Tan

Found 25 papers, 6 papers with code

Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery

no code implementations29 Nov 2021 Yan Du, Qiuhua Huang, Renke Huang, Tianzhixi Yin, Jie Tan, Wenhao Yu, Xinya Li

Reinforcement learning methods have been developed for the same or similar challenging control problems, but they suffer from training inefficiency and lack of robustness for "corner or unseen" scenarios.

Fast and Efficient Locomotion via Learned Gait Transitions

1 code implementation9 Apr 2021 Yuxiang Yang, Tingnan Zhang, Erwin Coumans, Jie Tan, Byron Boots

We focus on the problem of developing energy efficient controllers for quadrupedal robots.

How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned

no code implementations4 Feb 2021 Julian Ibarz, Jie Tan, Chelsea Finn, Mrinal Kalakrishnan, Peter Pastor, Sergey Levine

Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains.

Learning to be Safe: Deep RL with a Safety Critic

no code implementations27 Oct 2020 Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha, Jie Tan, Chelsea Finn

Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself.

Transfer Learning

Learning Agile Locomotion via Adversarial Training

no code implementations3 Aug 2020 Yujin Tang, Jie Tan, Tatsuya Harada

In contrast to prior works that used only one adversary, we find that training an ensemble of adversaries, each of which specializes in a different escaping strategy, is essential for the protagonist to master agility.

Legged Robots

Accelerated Deep Reinforcement Learning Based Load Shedding for Emergency Voltage Control

no code implementations22 Jun 2020 Renke Huang, Yujiao Chen, Tianzhixi Yin, Xinya Li, Ang Li, Jie Tan, Wenhao Yu, YuAn Liu, Qiuhua Huang

Load shedding has been one of the most widely used and effective emergency control approaches against voltage instability.

Learning Agile Robotic Locomotion Skills by Imitating Animals

no code implementations2 Apr 2020 Xue Bin Peng, Erwin Coumans, Tingnan Zhang, Tsang-Wei Lee, Jie Tan, Sergey Levine

In this work, we present an imitation learning system that enables legged robots to learn agile locomotion skills by imitating real-world animals.

Domain Adaptation Imitation Learning +1

Rapidly Adaptable Legged Robots via Evolutionary Meta-Learning

no code implementations2 Mar 2020 Xingyou Song, Yuxiang Yang, Krzysztof Choromanski, Ken Caluwaerts, Wenbo Gao, Chelsea Finn, Jie Tan

Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world.

Legged Robots Meta-Learning

Learning to Walk in the Real World with Minimal Human Effort

no code implementations20 Feb 2020 Sehoon Ha, Peng Xu, Zhenyu Tan, Sergey Levine, Jie Tan

In this paper, we develop a system for learning legged locomotion policies with deep RL in the real world with minimal human effort.

Legged Robots Multi-Task Learning

Policies Modulating Trajectory Generators

2 code implementations7 Oct 2019 Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke

We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller.

Learning Fast Adaptation with Meta Strategy Optimization

no code implementations28 Sep 2019 Wenhao Yu, Jie Tan, Yunfei Bai, Erwin Coumans, Sehoon Ha

The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases.

Legged Robots Meta-Learning

Zero-shot Imitation Learning from Demonstrations for Legged Robot Visual Navigation

no code implementations27 Sep 2019 Xinlei Pan, Tingnan Zhang, Brian Ichter, Aleksandra Faust, Jie Tan, Sehoon Ha

Here, we propose a zero-shot imitation learning approach for training a visual navigation policy on legged robots from human (third-person perspective) demonstrations, enabling high-quality navigation and cost-effective data collection.

Imitation Learning Legged Robots +1

Data Efficient Reinforcement Learning for Legged Robots

no code implementations8 Jul 2019 Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Tingnan Zhang, Jie Tan, Vikas Sindhwani

We present a model-based framework for robot locomotion that achieves walking based on only 4. 5 minutes (45, 000 control steps) of data collected on a quadruped robot.

Legged Robots Safe Exploration

Adaptive Power System Emergency Control using Deep Reinforcement Learning

1 code implementation9 Mar 2019 Qiuhua Huang, Renke Huang, Weituo Hao, Jie Tan, Rui Fan, Zhenyu Huang

Power system emergency control is generally regarded as the last safety net for grid security and resiliency.

NoRML: No-Reward Meta Learning

1 code implementation4 Mar 2019 Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Jie Tan, Chelsea Finn

To this end, we introduce a method that allows for self-adaptation of learned policies: No-Reward Meta Learning (NoRML).

Meta-Learning

Learning to Walk via Deep Reinforcement Learning

no code implementations26 Dec 2018 Tuomas Haarnoja, Sehoon Ha, Aurick Zhou, Jie Tan, George Tucker, Sergey Levine

In this paper, we propose a sample-efficient deep RL algorithm based on maximum entropy RL that requires minimal per-task tuning and only a modest number of trials to learn neural network policies.

Legged Robots

Large-Scale Evolution of Image Classifiers

2 code implementations ICML 2017 Esteban Real, Sherry Moore, Andrew Selle, Saurabh Saxena, Yutaka Leon Suematsu, Jie Tan, Quoc Le, Alex Kurakin

Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone.

Hyperparameter Optimization Image Classification +1

Preparing for the Unknown: Learning a Universal Policy with Online System Identification

no code implementations8 Feb 2017 Wenhao Yu, Jie Tan, C. Karen Liu, Greg Turk

Together, UP-OSI is a robust control policy that can be used across a wide range of dynamic models, and that is also responsive to sudden changes in the environment.

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