no code implementations • 18 Sep 2023 • Yevgen Chebotar, Quan Vuong, Alex Irpan, Karol Hausman, Fei Xia, Yao Lu, Aviral Kumar, Tianhe Yu, Alexander Herzog, Karl Pertsch, Keerthana Gopalakrishnan, Julian Ibarz, Ofir Nachum, Sumedh Sontakke, Grecia Salazar, Huong T Tran, Jodilyn Peralta, Clayton Tan, Deeksha Manjunath, Jaspiar Singht, Brianna Zitkovich, Tomas Jackson, Kanishka Rao, Chelsea Finn, Sergey Levine
In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data.
1 code implementation • 28 Jul 2023 • Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, Pete Florence, Chuyuan Fu, Montse Gonzalez Arenas, Keerthana Gopalakrishnan, Kehang Han, Karol Hausman, Alexander Herzog, Jasmine Hsu, Brian Ichter, Alex Irpan, Nikhil Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Lisa Lee, Tsang-Wei Edward Lee, Sergey Levine, Yao Lu, Henryk Michalewski, Igor Mordatch, Karl Pertsch, Kanishka Rao, Krista Reymann, Michael Ryoo, Grecia Salazar, Pannag Sanketi, Pierre Sermanet, Jaspiar Singh, Anikait Singh, Radu Soricut, Huong Tran, Vincent Vanhoucke, Quan Vuong, Ayzaan Wahid, Stefan Welker, Paul Wohlhart, Jialin Wu, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich
Our goal is to enable a single end-to-end trained model to both learn to map robot observations to actions and enjoy the benefits of large-scale pretraining on language and vision-language data from the web.
1 code implementation • 24 Jul 2023 • Kyle Hatch, Benjamin Eysenbach, Rafael Rafailov, Tianhe Yu, Ruslan Salakhutdinov, Sergey Levine, Chelsea Finn
In this paper, we propose a method for offline, example-based control that learns an implicit model of multi-step transitions, rather than a reward function.
1 code implementation • 1 Jun 2023 • Gaoyue Zhou, Victoria Dean, Mohan Kumar Srirama, Aravind Rajeswaran, Jyothish Pari, Kyle Hatch, Aryan Jain, Tianhe Yu, Pieter Abbeel, Lerrel Pinto, Chelsea Finn, Abhinav Gupta
Three challenges limit the progress of robot learning research: robots are expensive (few labs can participate), everyone uses different robots (findings do not generalize across labs), and we lack internet-scale robotics data.
2 code implementations • 6 Mar 2023 • Danny Driess, Fei Xia, Mehdi S. M. Sajjadi, Corey Lynch, Aakanksha Chowdhery, Brian Ichter, Ayzaan Wahid, Jonathan Tompson, Quan Vuong, Tianhe Yu, Wenlong Huang, Yevgen Chebotar, Pierre Sermanet, Daniel Duckworth, Sergey Levine, Vincent Vanhoucke, Karol Hausman, Marc Toussaint, Klaus Greff, Andy Zeng, Igor Mordatch, Pete Florence
Large language models excel at a wide range of complex tasks.
Ranked #1 on
Visual Question Answering (VQA)
on OK-VQA
no code implementations • 5 Mar 2023 • Lantao Yu, Tianhe Yu, Jiaming Song, Willie Neiswanger, Stefano Ermon
In this case, a well-known issue is the distribution shift between the learned policy and the behavior policy that collects the offline data.
no code implementations • 22 Feb 2023 • Tianhe Yu, Ted Xiao, Austin Stone, Jonathan Tompson, Anthony Brohan, Su Wang, Jaspiar Singh, Clayton Tan, Dee M, Jodilyn Peralta, Brian Ichter, Karol Hausman, Fei Xia
Specifically, we make use of the state of the art text-to-image diffusion models and perform aggressive data augmentation on top of our existing robotic manipulation datasets via inpainting various unseen objects for manipulation, backgrounds, and distractors with text guidance.
1 code implementation • 13 Dec 2022 • Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Joseph Dabis, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Tomas Jackson, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Isabel Leal, Kuang-Huei Lee, Sergey Levine, Yao Lu, Utsav Malla, Deeksha Manjunath, Igor Mordatch, Ofir Nachum, Carolina Parada, Jodilyn Peralta, Emily Perez, Karl Pertsch, Jornell Quiambao, Kanishka Rao, Michael Ryoo, Grecia Salazar, Pannag Sanketi, Kevin Sayed, Jaspiar Singh, Sumedh Sontakke, Austin Stone, Clayton Tan, Huong Tran, Vincent Vanhoucke, Steve Vega, Quan Vuong, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Tianhe Yu, Brianna Zitkovich
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance.
no code implementations • 26 Jul 2022 • Kaylee Burns, Tianhe Yu, Chelsea Finn, Karol Hausman
In this paper, we focus on one particular aspect of heterogeneity: learning from offline data collected at different control frequencies.
no code implementations • 16 Mar 2022 • Xi Chen, Ali Ghadirzadeh, Tianhe Yu, Yuan Gao, Jianhao Wang, Wenzhe Li, Bin Liang, Chelsea Finn, Chongjie Zhang
Offline reinforcement learning methods hold the promise of learning policies from pre-collected datasets without the need to query the environment for new transitions.
no code implementations • 3 Feb 2022 • Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Chelsea Finn, Sergey Levine
One natural solution is to learn a reward function from the labeled data and use it to label the unlabeled data.
no code implementations • 29 Sep 2021 • Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Chelsea Finn, Sergey Levine, Karol Hausman
However, these benefits come at a cost -- for data to be shared between tasks, each transition must be annotated with reward labels corresponding to other tasks.
no code implementations • NeurIPS 2021 • Tianhe Yu, Aviral Kumar, Yevgen Chebotar, Karol Hausman, Sergey Levine, Chelsea Finn
We argue that a natural use case of offline RL is in settings where we can pool large amounts of data collected in various scenarios for solving different tasks, and utilize all of this data to learn behaviors for all the tasks more effectively rather than training each one in isolation.
1 code implementation • NeurIPS 2021 • Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn
Multi-task learning can leverage information learned by one task to benefit the training of other tasks.
no code implementations • NeurIPS 2021 • Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
We consider a setting where an agent is provided a fixed dataset of visual demonstrations illustrating how to perform a task, and must learn to solve the task using the provided demonstrations and unsupervised environment interactions.
no code implementations • ICLR Workshop SSL-RL 2021 • Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
We consider the problem setting of imitation learning where the agent is provided a fixed dataset of demonstrations.
4 code implementations • NeurIPS 2021 • Tianhe Yu, Aviral Kumar, Rafael Rafailov, Aravind Rajeswaran, Sergey Levine, Chelsea Finn
We overcome this limitation by developing a new model-based offline RL algorithm, COMBO, that regularizes the value function on out-of-support state-action tuples generated via rollouts under the learned model.
no code implementations • 1 Jan 2021 • Tianhe Yu, Xinyang Geng, Chelsea Finn, Sergey Levine
Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.
no code implementations • 1 Jan 2021 • Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn
Multi-task learning can leverage information learned by one task to benefit the training of other tasks.
1 code implementation • 21 Dec 2020 • Rafael Rafailov, Tianhe Yu, Aravind Rajeswaran, Chelsea Finn
In this work, we build on recent advances in model-based algorithms for offline RL, and extend them to high-dimensional visual observation spaces.
no code implementations • 14 Dec 2020 • Tianhe Yu, Xinyang Geng, Chelsea Finn, Sergey Levine
Few-shot meta-learning methods consider the problem of learning new tasks from a small, fixed number of examples, by meta-learning across static data from a set of previous tasks.
no code implementations • 29 Oct 2020 • Christopher Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, Chelsea Finn
Multi-task learning can leverage information learned by one task to benefit the training of other tasks.
4 code implementations • NeurIPS 2020 • Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Zou, Sergey Levine, Chelsea Finn, Tengyu Ma
We also characterize the trade-off between the gain and risk of leaving the support of the batch data.
9 code implementations • NeurIPS 2020 • Tianhe Yu, Saurabh Kumar, Abhishek Gupta, Sergey Levine, Karol Hausman, Chelsea Finn
While deep learning and deep reinforcement learning (RL) systems have demonstrated impressive results in domains such as image classification, game playing, and robotic control, data efficiency remains a major challenge.
8 code implementations • 24 Oct 2019 • Tianhe Yu, Deirdre Quillen, Zhanpeng He, Ryan Julian, Avnish Narayan, Hayden Shively, Adithya Bellathur, Karol Hausman, Chelsea Finn, Sergey Levine
Therefore, if the aim of these methods is to enable faster acquisition of entirely new behaviors, we must evaluate them on task distributions that are sufficiently broad to enable generalization to new behaviors.
Ranked #1 on
Meta-Learning
on ML10
no code implementations • 25 Sep 2019 • Tianhe Yu, Saurabh Kumar, Eric Mitchell, Abhishek Gupta, Karol Hausman, Sergey Levine, Chelsea Finn
Deep learning enables training of large and flexible function approximators from scratch at the cost of large amounts of data.
1 code implementation • NeurIPS 2019 • Lantao Yu, Tianhe Yu, Chelsea Finn, Stefano Ermon
Critically, our model can infer rewards for new, structurally-similar tasks from a single demonstration.
Ranked #1 on
MuJoCo Games
on Sawyer Pusher
no code implementations • ICLR 2019 • Rosen Kralev, Russell Mendonca, Alvin Zhang, Tianhe Yu, Abhishek Gupta, Pieter Abbeel, Sergey Levine, Chelsea Finn
Meta-reinforcement learning aims to learn fast reinforcement learning (RL) procedures that can be applied to new tasks or environments.
1 code implementation • 14 Feb 2019 • Tianhe Yu, Gleb Shevchuk, Dorsa Sadigh, Chelsea Finn
While reinforcement learning (RL) has the potential to enable robots to autonomously acquire a wide range of skills, in practice, RL usually requires manual, per-task engineering of reward functions, especially in real world settings where aspects of the environment needed to compute progress are not directly accessible.
no code implementations • 25 Oct 2018 • Tianhe Yu, Pieter Abbeel, Sergey Levine, Chelsea Finn
We consider the problem of learning multi-stage vision-based tasks on a real robot from a single video of a human performing the task, while leveraging demonstration data of subtasks with other objects.
2 code implementations • 5 Feb 2018 • Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, Sergey Levine
Humans and animals are capable of learning a new behavior by observing others perform the skill just once.
3 code implementations • 14 Sep 2017 • Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, Sergey Levine
In this work, we present a meta-imitation learning method that enables a robot to learn how to learn more efficiently, allowing it to acquire new skills from just a single demonstration.
3 code implementations • 8 May 2017 • Richard Zhang, Jun-Yan Zhu, Phillip Isola, Xinyang Geng, Angela S. Lin, Tianhe Yu, Alexei A. Efros
The system directly maps a grayscale image, along with sparse, local user "hints" to an output colorization with a Convolutional Neural Network (CNN).
no code implementations • 1 Dec 2016 • Chelsea Finn, Tianhe Yu, Justin Fu, Pieter Abbeel, Sergey Levine
We evaluate our method on challenging tasks that require control directly from images, and show that our approach can improve the generalization of a learned deep neural network policy by using experience for which no reward function is available.