1 code implementation • 30 May 2022 • Kuang-Huei Lee, Ofir Nachum, Mengjiao Yang, Lisa Lee, Daniel Freeman, Winnie Xu, Sergio Guadarrama, Ian Fischer, Eric Jang, Henryk Michalewski, Igor Mordatch
Specifically, we show that a single transformer-based model - with a single set of weights - trained purely offline can play a suite of up to 46 Atari games simultaneously at close-to-human performance.
3 code implementations • 4 Apr 2022 • Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, Mengyuan Yan, Andy Zeng
We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.
no code implementations • 15 Feb 2022 • Yuqing Du, Daniel Ho, Alexander A. Alemi, Eric Jang, Mohi Khansari
In this work we investigate and demonstrate benefits of a Bayesian approach to imitation learning from multiple sensor inputs, as applied to the task of opening office doors with a mobile manipulator.
no code implementations • 4 Feb 2022 • Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, Chelsea Finn
In this paper, we study the problem of enabling a vision-based robotic manipulation system to generalize to novel tasks, a long-standing challenge in robot learning.
no code implementations • 3 Feb 2022 • Mohi Khansari, Daniel Ho, Yuqing Du, Armando Fuentes, Matthew Bennice, Nicolas Sievers, Sean Kirmani, Yunfei Bai, Eric Jang
To the best of our knowledge, this is the first work to tackle latched door opening from a purely end-to-end learning approach, where the task of navigation and manipulation are jointly modeled by a single neural network.
1 code implementation • NeurIPS 2020 • Janarthanan Rajendran, Alex Irpan, Eric Jang
Meta-learning algorithms aim to learn two components: a model that predicts targets for a task, and a base learner that quickly updates that model when given examples from a new task.
no code implementations • ICLR 2020 • Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn
Imitation learning allows agents to learn complex behaviors from demonstrations.
no code implementations • ICLR 2020 • Ted Xiao, Eric Jang, Dmitry Kalashnikov, Sergey Levine, Julian Ibarz, Karol Hausman, Alexander Herzog
We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robot must decide on the next action while still performing the previous action.
no code implementations • 25 Feb 2020 • Avi Singh, Eric Jang, Alexander Irpan, Daniel Kappler, Murtaza Dalal, Sergey Levine, Mohi Khansari, Chelsea Finn
In this work, we target this challenge, aiming to build an imitation learning system that can continuously improve through autonomous data collection, while simultaneously avoiding the explicit use of reinforcement learning, to maintain the stability, simplicity, and scalability of supervised imitation.
no code implementations • 7 Jun 2019 • Allan Zhou, Eric Jang, Daniel Kappler, Alex Herzog, Mohi Khansari, Paul Wohlhart, Yunfei Bai, Mrinal Kalakrishnan, Sergey Levine, Chelsea Finn
Imitation learning allows agents to learn complex behaviors from demonstrations.
1 code implementation • 16 Nov 2018 • Eric Jang, Coline Devin, Vincent Vanhoucke, Sergey Levine
We formulate an arithmetic relationship between feature vectors from this observation, and use it to learn a representation of scenes and objects that can then be used to identify object instances, localize them in the scene, and perform goal-directed grasping tasks where the robot must retrieve commanded objects from a bin.
1 code implementation • 2 Oct 2018 • Hyunsun Choi, Eric Jang, Alexander A. Alemi
Machine learning models encounter Out-of-Distribution (OoD) errors when the data seen at test time are generated from a different stochastic generator than the one used to generate the training data.
1 code implementation • 27 Jun 2018 • Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, Sergey Levine
In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach.
no code implementations • CVPR 2018 • Fereshteh Sadeghi, Alexander Toshev, Eric Jang, Sergey Levine
In robotics, this ability is referred to as visual servoing: moving a tool or end-point to a desired location using primarily visual feedback.
1 code implementation • 28 Feb 2018 • Deirdre Quillen, Eric Jang, Ofir Nachum, Chelsea Finn, Julian Ibarz, Sergey Levine
In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping.
no code implementations • 20 Dec 2017 • Fereshteh Sadeghi, Alexander Toshev, Eric Jang, Sergey Levine
To this end, we train a deep recurrent controller that can automatically determine which actions move the end-point of a robotic arm to a desired object.
no code implementations • 6 Jul 2017 • Eric Jang, Sudheendra Vijayanarasimhan, Peter Pastor, Julian Ibarz, Sergey Levine
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images.
7 code implementations • 23 Apr 2017 • Pierre Sermanet, Corey Lynch, Yevgen Chebotar, Jasmine Hsu, Eric Jang, Stefan Schaal, Sergey Levine
While representations are learned from an unlabeled collection of task-related videos, robot behaviors such as pouring are learned by watching a single 3rd-person demonstration by a human.
Ranked #3 on Video Alignment on UPenn Action
19 code implementations • 3 Nov 2016 • Eric Jang, Shixiang Gu, Ben Poole
Categorical variables are a natural choice for representing discrete structure in the world.
1 code implementation • ICLR 2017 2016 • Eric Jang, Shixiang Gu, Ben Poole
Categorical variables are a natural choice for representing discrete structure in the world.