no code implementations • 24 May 2023 • Ken Caluwaerts, Atil Iscen, J. Chase Kew, Wenhao Yu, Tingnan Zhang, Daniel Freeman, Kuang-Huei Lee, Lisa Lee, Stefano Saliceti, Vincent Zhuang, Nathan Batchelor, Steven Bohez, Federico Casarini, Jose Enrique Chen, Omar Cortes, Erwin Coumans, Adil Dostmohamed, Gabriel Dulac-Arnold, Alejandro Escontrela, Erik Frey, Roland Hafner, Deepali Jain, Bauyrjan Jyenis, Yuheng Kuang, Edward Lee, Linda Luu, Ofir Nachum, Ken Oslund, Jason Powell, Diego Reyes, Francesco Romano, Feresteh Sadeghi, Ron Sloat, Baruch Tabanpour, Daniel Zheng, Michael Neunert, Raia Hadsell, Nicolas Heess, Francesco Nori, Jeff Seto, Carolina Parada, Vikas Sindhwani, Vincent Vanhoucke, Jie Tan
In the second approach, we distill the specialist skills into a Transformer-based generalist locomotion policy, named Locomotion-Transformer, that can handle various terrains and adjust the robot's gait based on the perceived environment and robot states.
no code implementations • 19 Apr 2023 • Laura Smith, J. Chase Kew, Tianyu Li, Linda Luu, Xue Bin Peng, Sehoon Ha, Jie Tan, Sergey Levine
Legged robots have enormous potential in their range of capabilities, from navigating unstructured terrains to high-speed running.
no code implementations • 17 Apr 2023 • Yuxiang Yang, Xiangyun Meng, Wenhao Yu, Tingnan Zhang, Jie Tan, Byron Boots
Jumping is essential for legged robots to traverse through difficult terrains.
no code implementations • 2 Feb 2023 • Deepali Jain, Krzysztof Marcin Choromanski, Sumeet Singh, Vikas Sindhwani, Tingnan Zhang, Jie Tan, Avinava Dubey
Training complex machine learning (ML) architectures requires a compute and time consuming process of selecting the right optimizer and tuning its hyper-parameters.
no code implementations • 6 Dec 2022 • Ramij R. Hossain, Tianzhixi Yin, Yan Du, Renke Huang, Jie Tan, Wenhao Yu, YuAn Liu, Qiuhua Huang
We propose a novel model-based-DRL framework where a deep neural network (DNN)-based dynamic surrogate model, instead of a real-world power-grid or physics-based simulation, is utilized with the policy learning framework, making the process faster and sample efficient.
no code implementations • 19 Oct 2022 • Thomas Lew, Sumeet Singh, Mario Prats, Jeffrey Bingham, Jonathan Weisz, Benjie Holson, Xiaohan Zhang, Vikas Sindhwani, Yao Lu, Fei Xia, Peng Xu, Tingnan Zhang, Jie Tan, Montserrat Gonzalez
This problem is challenging, as it requires planning wiping actions while reasoning over uncertain latent dynamics of crumbs and spills captured via high-dimensional visual observations.
no code implementations • 22 Sep 2022 • Xuesu Xiao, Tingnan Zhang, Krzysztof Choromanski, Edward Lee, Anthony Francis, Jake Varley, Stephen Tu, Sumeet Singh, Peng Xu, Fei Xia, Sven Mikael Persson, Dmitry Kalashnikov, Leila Takayama, Roy Frostig, Jie Tan, Carolina Parada, Vikas Sindhwani
Despite decades of research, existing navigation systems still face real-world challenges when deployed in the wild, e. g., in cluttered home environments or in human-occupied public spaces.
no code implementations • 27 Jul 2022 • Kuang-Huei Lee, Ofir Nachum, Tingnan Zhang, Sergio Guadarrama, Jie Tan, Wenhao Yu
Evolution Strategy (ES) algorithms have shown promising results in training complex robotic control policies due to their massive parallelism capability, simple implementation, effective parameter-space exploration, and fast training time.
no code implementations • 27 Jun 2022 • Yuxiang Yang, Xiangyun Meng, Wenhao Yu, Tingnan Zhang, Jie Tan, Byron Boots
Using only 40 minutes of human demonstration data, our framework learns to adjust the speed and gait of the robot based on perceived terrain semantics, and enables the robot to walk over 6km without failure at close-to-optimal speed.
1 code implementation • 29 May 2022 • Zuxin Liu, Zijian Guo, Zhepeng Cen, huan zhang, Jie Tan, Bo Li, Ding Zhao
One interesting and counter-intuitive finding is that the maximum reward attack is strong, as it can both induce unsafe behaviors and make the attack stealthy by maintaining the reward.
no code implementations • 8 Apr 2022 • Juan Jose Garau-Luis, Yingjie Miao, John D. Co-Reyes, Aaron Parisi, Jie Tan, Esteban Real, Aleksandra Faust
Generalizability and stability are two key objectives for operating reinforcement learning (RL) agents in the real world.
no code implementations • 5 Mar 2022 • Tsung-Yen Yang, Tingnan Zhang, Linda Luu, Sehoon Ha, Jie Tan, Wenhao Yu
In this paper, we propose a safe reinforcement learning framework that switches between a safe recovery policy that prevents the robot from entering unsafe states, and a learner policy that is optimized to complete the task.
no code implementations • 7 Dec 2021 • Michael Luo, Ashwin Balakrishna, Brijen Thananjeyan, Suraj Nair, Julian Ibarz, Jie Tan, Chelsea Finn, Ion Stoica, Ken Goldberg
Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments.
no code implementations • 29 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.
no code implementations • 13 Apr 2021 • Zongshen Mu, Siliang Tang, Jie Tan, Qiang Yu, Yueting Zhuang
In this paper, we propose a novel graph learning framework for phrase grounding in the image.
Ranked #6 on Phrase Grounding on Flickr30k Entities Test
1 code implementation • 9 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.
no code implementations • 8 Feb 2021 • Krzysztof Marcin Choromanski, Deepali Jain, Wenhao Yu, Xingyou Song, Jack Parker-Holder, Tingnan Zhang, Valerii Likhosherstov, Aldo Pacchiano, Anirban Santara, Yunhao Tang, Jie Tan, Adrian Weller
There has recently been significant interest in training reinforcement learning (RL) agents in vision-based environments.
no code implementations • 4 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.
no code implementations • 13 Jan 2021 • Renke Huang, Yujiao Chen, Tianzhixi Yin, Qiuhua Huang, Jie Tan, Wenhao Yu, Xinya Li, Ang Li, Yan Du
In this paper, we mitigate these limitations by developing a novel deep meta reinforcement learning (DMRL) algorithm.
no code implementations • 27 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.
no code implementations • 3 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.
no code implementations • ACL 2020 • Jie Tan, Changlin Yang, Ying Li, Siliang Tang, Chen Huang, Yueting Zhuang
Measuring the scholarly impact of a document without citations is an important and challenging problem.
no code implementations • 22 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.
no code implementations • 2 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.
no code implementations • 15 Mar 2020 • Rose E. Wang, J. Chase Kew, Dennis Lee, Tsang-Wei Edward Lee, Tingnan Zhang, Brian Ichter, Jie Tan, Aleksandra Faust
We propose hierarchical predictive planning (HPP), a model-based reinforcement learning method for decentralized multiagent rendezvous.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 2 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.
1 code implementation • 20 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.
3 code implementations • 7 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.
1 code implementation • 28 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.
no code implementations • 27 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.
no code implementations • 8 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.
1 code implementation • 9 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.
1 code implementation • 4 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).
no code implementations • 26 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.
46 code implementations • 13 Dec 2018 • Tuomas Haarnoja, Aurick Zhou, Kristian Hartikainen, George Tucker, Sehoon Ha, Jie Tan, Vikash Kumar, Henry Zhu, Abhishek Gupta, Pieter Abbeel, Sergey Levine
A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
no code implementations • 27 Apr 2018 • Jie Tan, Tingnan Zhang, Erwin Coumans, Atil Iscen, Yunfei Bai, Danijar Hafner, Steven Bohez, Vincent Vanhoucke
The control policies are learned in a physics simulator and then deployed on real robots.
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.
Ranked #118 on Image Classification on CIFAR-10
1 code implementation • 8 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.