Search Results - "Ibarz, Julian"

How to train your robot with deep reinforcement learning: lessons we have learned by Ibarz, Julian, Tan, Jie, Finn, Chelsea, Kalakrishnan, Mrinal, Pastor, Peter, Levine, Sergey

“…Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although…”
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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection by Levine, Sergey, Pastor, Peter, Krizhevsky, Alex, Ibarz, Julian, Quillen, Deirdre

“…We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we…”
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Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks by James, Stephen, Wohlhart, Paul, Kalakrishnan, Mrinal, Kalashnikov, Dmitry, Irpan, Alex, Ibarz, Julian, Levine, Sergey, Hadsell, Raia, Bousmalis, Konstantinos

“…Real world data, especially in the domain of robotics, is notoriously costly to collect. One way to circumvent this can be to leverage the power of simulation…”
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Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods by Quillen, Deirdre, Jang, Eric, Nachum, Ofir, Finn, Chelsea, Ibarz, Julian, Levine, Sergey

“…In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping. Model-free deep reinforcement learning (RL) has been…”
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Attention-Based Extraction of Structured Information from Street View Imagery by Wojna, Zbigniew, Gorban, Alexander N., Dar-Shyang Lee, Murphy, Kevin, Qian Yu, Yeqing Li, Ibarz, Julian

“…We present a neural network model - based on Convolutional Neural Networks, Recurrent Neural Networks and a novel attention mechanism - which achieves 84.2%…”
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RL-CycleGAN: Reinforcement Learning Aware Simulation-to-Real by Rao, Kanishka, Harris, Chris, Irpan, Alex, Levine, Sergey, Ibarz, Julian, Khansari, Mohi

“…Deep neural network based reinforcement learning (RL) can learn appropriate visual representations for complex tasks like vision-based robotic grasping without…”
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Recovery RL: Safe Reinforcement Learning With Learned Recovery Zones by Thananjeyan, Brijen, Balakrishna, Ashwin, Nair, Suraj, Luo, Michael, Srinivasan, Krishnan, Hwang, Minho, Gonzalez, Joseph E., Ibarz, Julian, Finn, Chelsea, Goldberg, Ken

“…Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive…”
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Token Turing Machines by Ryoo, Michael S., Gopalakrishnan, Keerthana, Kahatapitiya, Kumara, Xiao, Ted, Rao, Kanishka, Stone, Austin, Lu, Yao, Ibarz, Julian, Arnab, Anurag

“…We propose Token Turing Machines (TTM), a sequential, autoregressive Transformer model with memory for real-world sequential visual understanding. Our model is…”
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Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping by Bousmalis, Konstantinos, Irpan, Alex, Wohlhart, Paul, Bai, Yunfei, Kelcey, Matthew, Kalakrishnan, Mrinal, Downs, Laura, Ibarz, Julian, Pastor, Peter, Konolige, Kurt, Levine, Sergey, Vanhoucke, Vincent

“…Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An…”
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Esophagus Silhouette Extraction and Reconstruction From Fluoroscopic Views for Cardiac Ablation Procedure Guidance by Yatziv, L., Ibarz, J., Strobel, N., Datta, S., Sapiro, G.

“…Cardiac ablation involves the risk of serious complications when thermal injury to the esophagus occurs. This paper proposes to reduce the risk of such…”
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Visionary: Vision architecture discovery for robot learning by Akinola, Iretiayo, Angelova, Anelia, Lu, Yao, Chebotar, Yevgen, Kalashnikov, Dmitry, Varley, Jacob, Ibarz, Julian, Ryoo, Michael S.

“…We propose a vision-based architecture search algorithm for robot manipulation learning, which discovers interactions between low dimension action inputs and…”
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Token Turing Machines by Ryoo, Michael S, Gopalakrishnan, Keerthana, Kahatapitiya, Kumara, Xiao, Ted, Rao, Kanishka, Stone, Austin, Lu, Yao, Ibarz, Julian, Arnab, Anurag

“…CVPR 2023 We propose Token Turing Machines (TTM), a sequential, autoregressive Transformer model with memory for real-world sequential visual understanding…”
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How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned by Ibarz, Julian, Tan, Jie, Finn, Chelsea, Kalakrishnan, Mrinal, Pastor, Peter, Levine, Sergey

“…Journal of Robotics Research (IJRR), February 2021 Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex…”
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MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance by Luo, Michael, Balakrishna, Ashwin, Thananjeyan, Brijen, Nair, Suraj, Ibarz, Julian, Tan, Jie, Finn, Chelsea, Stoica, Ion, Goldberg, Ken

“…Workshop on Safe and Robust Control of Uncertain Systems at the 35th Conference on Neural Information Processing Systems (NeurIPS 2021), Online Safe…”
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RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real by Rao, Kanishka, Harris, Chris, Irpan, Alex, Levine, Sergey, Ibarz, Julian, Khansari, Mohi

“…Deep neural network based reinforcement learning (RL) can learn appropriate visual representations for complex tasks like vision-based robotic grasping without…”
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Visionary: Vision architecture discovery for robot learning by Akinola, Iretiayo, Angelova, Anelia, Lu, Yao, Chebotar, Yevgen, Kalashnikov, Dmitry, Varley, Jacob, Ibarz, Julian, Ryoo, Michael S

“…ICRA 2021 We propose a vision-based architecture search algorithm for robot manipulation learning, which discovers interactions between low dimension action…”
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Thinking While Moving: Deep Reinforcement Learning with Concurrent Control by Xiao, Ted, Jang, Eric, Kalashnikov, Dmitry, Levine, Sergey, Ibarz, Julian, Hausman, Karol, Herzog, Alexander

“…We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled…”
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Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions by Chebotar, Yevgen, Vuong, Quan, Irpan, Alex, Hausman, Karol, Xia, Fei, Lu, Yao, Kumar, Aviral, Yu, Tianhe, Herzog, Alexander, Pertsch, Karl, Gopalakrishnan, Keerthana, Ibarz, Julian, Nachum, Ofir, Sontakke, Sumedh, Salazar, Grecia, Tran, Huong T, Peralta, Jodilyn, Tan, Clayton, Manjunath, Deeksha, Singht, Jaspiar, Zitkovich, Brianna, Jackson, Tomas, Rao, Kanishka, Finn, Chelsea, Levine, Sergey

“…In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human…”
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Diversity is All You Need: Learning Skills without a Reward Function by Eysenbach, Benjamin, Gupta, Abhishek, Ibarz, Julian, Levine, Sergey

“…Intelligent creatures can explore their environments and learn useful skills without supervision. In this paper, we propose DIAYN ('Diversity is All You…”
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Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning by Eysenbach, Benjamin, Gu, Shixiang, Ibarz, Julian, Levine, Sergey

“…Deep reinforcement learning algorithms can learn complex behavioral skills, but real-world application of these methods requires a large amount of experience…”
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