Search Results for author: Julian Ibarz

Found 22 papers, 9 papers with code

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances

no code implementations4 Apr 2022 Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, 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

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.

Decision Making Language Modelling

Visionary: Vision architecture discovery for robot learning

no code implementations26 Mar 2021 Iretiayo Akinola, Anelia Angelova, Yao Lu, Yevgen Chebotar, Dmitry Kalashnikov, Jacob Varley, Julian Ibarz, Michael S. Ryoo

We propose a vision-based architecture search algorithm for robot manipulation learning, which discovers interactions between low dimension action inputs and high dimensional visual inputs.

Neural Architecture Search

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.

reinforcement-learning

Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones

1 code implementation29 Oct 2020 Brijen Thananjeyan, Ashwin Balakrishna, Suraj Nair, Michael Luo, Krishnan Srinivasan, Minho Hwang, Joseph E. Gonzalez, Julian Ibarz, Chelsea Finn, Ken Goldberg

Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration.

reinforcement-learning Safe Reinforcement Learning

RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real

no code implementations CVPR 2020 Kanishka Rao, Chris Harris, Alex Irpan, Sergey Levine, Julian Ibarz, Mohi Khansari

However, this sort of translation is typically task-agnostic, in that the translated images may not preserve all features that are relevant to the task.

reinforcement-learning Robotic Grasping +1

Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

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.

reinforcement-learning Robotic Grasping

Off-Policy Evaluation via Off-Policy Classification

no code implementations NeurIPS 2019 Alex Irpan, Kanishka Rao, Konstantinos Bousmalis, Chris Harris, Julian Ibarz, Sergey Levine

However, for high-dimensional observations, such as images, models of the environment can be difficult to fit and value-based methods can make IS hard to use or even ill-conditioned, especially when dealing with continuous action spaces.

Classification General Classification +1

Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning

1 code implementation ICLR 2018 Benjamin Eysenbach, Shixiang Gu, Julian Ibarz, Sergey Levine

In this work, we propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and reset policy, with the reset policy resetting the environment for a subsequent attempt.

reinforcement-learning

Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

1 code implementation22 Sep 2017 Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, Sergey Levine, Vincent Vanhoucke

We extensively evaluate our approaches with a total of more than 25, 000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN.

Domain Adaptation Industrial Robots +1

End-to-End Learning of Semantic Grasping

no code implementations6 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.

Object Detection Robotic Grasping +1

Discrete Sequential Prediction of Continuous Actions for Deep RL

no code implementations ICLR 2018 Luke Metz, Julian Ibarz, Navdeep Jaitly, James Davidson

Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions.

Continuous Control Q-Learning +1

Attention-based Extraction of Structured Information from Street View Imagery

4 code implementations11 Apr 2017 Zbigniew Wojna, Alex Gorban, Dar-Shyang Lee, Kevin Murphy, Qian Yu, Yeqing Li, Julian Ibarz

We present a neural network model - based on CNNs, RNNs and a novel attention mechanism - which achieves 84. 2% accuracy on the challenging French Street Name Signs (FSNS) dataset, significantly outperforming the previous state of the art (Smith'16), which achieved 72. 46%.

Optical Character Recognition

End-to-End Interpretation of the French Street Name Signs Dataset

4 code implementations13 Feb 2017 Raymond Smith, Chunhui Gu, Dar-Shyang Lee, Huiyi Hu, Ranjith Unnikrishnan, Julian Ibarz, Sacha Arnoud, Sophia Lin

We introduce the French Street Name Signs (FSNS) Dataset consisting of more than a million images of street name signs cropped from Google Street View images of France.

Optical Character Recognition

Large Scale Business Discovery from Street Level Imagery

no code implementations17 Dec 2015 Qian Yu, Christian Szegedy, Martin C. Stumpe, Liron Yatziv, Vinay Shet, Julian Ibarz, Sacha Arnoud

Precise business store front detection enables accurate geo-location of businesses, and further provides input for business categorization, listing generation, etc.

Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

17 code implementations20 Dec 2013 Ian J. Goodfellow, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, Vinay Shet

In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels.

Image Classification

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