Search Results - "Irpan, Alex"

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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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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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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The Principle of Unchanged Optimality in Reinforcement Learning Generalization by Irpan, Alex, Song, Xingyou

“…Several recent papers have examined generalization in reinforcement learning (RL), by proposing new environments or ways to add noise to existing environments,…”
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STEER: Flexible Robotic Manipulation via Dense Language Grounding by Smith, Laura, Irpan, Alex, Arenas, Montserrat Gonzalez, Kirmani, Sean, Kalashnikov, Dmitry, Shah, Dhruv, Xiao, Ted

“…The complexity of the real world demands robotic systems that can intelligently adapt to unseen situations. We present STEER, a robot learning framework that…”
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Meta-Learning Requires Meta-Augmentation by Rajendran, Janarthanan, Irpan, Alex, Jang, Eric

“…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…”
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Stop Regressing: Training Value Functions via Classification for Scalable Deep RL by Farebrother, Jesse, Orbay, Jordi, Vuong, Quan, Taïga, Adrien Ali, Chebotar, Yevgen, Xiao, Ted, Irpan, Alex, Levine, Sergey, Castro, Pablo Samuel, Faust, Aleksandra, Kumar, Aviral, Agarwal, Rishabh

“…Value functions are a central component of deep reinforcement learning (RL). These functions, parameterized by neural networks, are trained using a mean…”
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BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning by Jang, Eric, Irpan, Alex, Khansari, Mohi, Kappler, Daniel, Ebert, Frederik, Lynch, Corey, Levine, Sergey, Finn, Chelsea

“…Conference on Robot Learning (pp. 991-1002). 2022 Jan 11 In this paper, we study the problem of enabling a vision-based robotic manipulation system to…”
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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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AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents by Ahn, Michael, Dwibedi, Debidatta, Finn, Chelsea, Arenas, Montse Gonzalez, Gopalakrishnan, Keerthana, Hausman, Karol, Ichter, Brian, Irpan, Alex, Joshi, Nikhil, Julian, Ryan, Kirmani, Sean, Leal, Isabel, Lee, Edward, Levine, Sergey, Lu, Yao, Maddineni, Sharath, Rao, Kanishka, Sadigh, Dorsa, Sanketi, Pannag, Sermanet, Pierre, Vuong, Quan, Welker, Stefan, Xia, Fei, Xiao, Ted, Xu, Peng, Xu, Steve, Xu, Zhuo

“…Foundation models that incorporate language, vision, and more recently actions have revolutionized the ability to harness internet scale data to reason about…”
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Noise Contrastive Priors for Functional Uncertainty by Hafner, Danijar, Tran, Dustin, Lillicrap, Timothy, Irpan, Alex, Davidson, James

“…Obtaining reliable uncertainty estimates of neural network predictions is a long standing challenge. Bayesian neural networks have been proposed as a solution,…”
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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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AW-Opt: Learning Robotic Skills with Imitation and Reinforcement at Scale by Lu, Yao, Hausman, Karol, Chebotar, Yevgen, Yan, Mengyuan, Jang, Eric, Herzog, Alexander, Xiao, Ted, Irpan, Alex, Khansari, Mohi, Kalashnikov, Dmitry, Levine, Sergey

“…Robotic skills can be learned via imitation learning (IL) using user-provided demonstrations, or via reinforcement learning (RL) using large amountsof…”
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Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills by Chebotar, Yevgen, Hausman, Karol, Lu, Yao, Xiao, Ted, Kalashnikov, Dmitry, Varley, Jake, Irpan, Alex, Eysenbach, Benjamin, Julian, Ryan, Finn, Chelsea, Levine, Sergey

“…We consider the problem of learning useful robotic skills from previously collected offline data without access to manually specified rewards or additional…”
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Off-Policy Evaluation via Off-Policy Classification by Irpan, Alex, Rao, Kanishka, Bousmalis, Konstantinos, Harris, Chris, Ibarz, Julian, Levine, Sergey

“…In this work, we consider the problem of model selection for deep reinforcement learning (RL) in real-world environments. Typically, the performance of deep RL…”
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Learning Hierarchical Information Flow with Recurrent Neural Modules by Hafner, Danijar, Irpan, Alex, Davidson, James, Heess, Nicolas

“…We propose ThalNet, a deep learning model inspired by neocortical communication via the thalamus. Our model consists of recurrent neural modules that send…”
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RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control by Brohan, Anthony, Brown, Noah, Carbajal, Justice, Chebotar, Yevgen, Chen, Xi, Choromanski, Krzysztof, Ding, Tianli, Driess, Danny, Dubey, Avinava, Finn, Chelsea, Florence, Pete, Fu, Chuyuan, Arenas, Montse Gonzalez, Gopalakrishnan, Keerthana, Han, Kehang, Hausman, Karol, Herzog, Alexander, Hsu, Jasmine, Ichter, Brian, Irpan, Alex, Joshi, Nikhil, Julian, Ryan, Kalashnikov, Dmitry, Kuang, Yuheng, Leal, Isabel, Lee, Lisa, Lee, Tsang-Wei Edward, Levine, Sergey, Lu, Yao, Michalewski, Henryk, Mordatch, Igor, Pertsch, Karl, Rao, Kanishka, Reymann, Krista, Ryoo, Michael, Salazar, Grecia, Sanketi, Pannag, Sermanet, Pierre, Singh, Jaspiar, Singh, Anikait, Soricut, Radu, Tran, Huong, Vanhoucke, Vincent, Vuong, Quan, Wahid, Ayzaan, Welker, Stefan, Wohlhart, Paul, Wu, Jialin, Xia, Fei, Xiao, Ted, Xu, Peng, Xu, Sichun, Yu, Tianhe, Zitkovich, Brianna

“…We study how vision-language models trained on Internet-scale data can be incorporated directly into end-to-end robotic control to boost generalization and…”
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RT-1: Robotics Transformer for Real-World Control at Scale by Brohan, Anthony, Brown, Noah, Carbajal, Justice, Chebotar, Yevgen, Dabis, Joseph, Finn, Chelsea, Gopalakrishnan, Keerthana, Hausman, Karol, Herzog, Alex, Hsu, Jasmine, Ibarz, Julian, Ichter, Brian, Irpan, Alex, Jackson, Tomas, Jesmonth, Sally, Joshi, Nikhil J, Julian, Ryan, Kalashnikov, Dmitry, Kuang, Yuheng, Leal, Isabel, Lee, Kuang-Huei, Levine, Sergey, Lu, Yao, Malla, Utsav, Manjunath, Deeksha, Mordatch, Igor, Nachum, Ofir, Parada, Carolina, Peralta, Jodilyn, Perez, Emily, Pertsch, Karl, Quiambao, Jornell, Rao, Kanishka, Ryoo, Michael, Salazar, Grecia, Sanketi, Pannag, Sayed, Kevin, Singh, Jaspiar, Sontakke, Sumedh, Stone, Austin, Tan, Clayton, Tran, Huong, Vanhoucke, Vincent, Vega, Steve, Vuong, Quan, Xia, Fei, Xiao, Ted, Xu, Peng, Xu, Sichun, Yu, Tianhe, Zitkovich, Brianna

“…By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or…”
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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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Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? by Raghu, Maithra, Irpan, Alex, Andreas, Jacob, Kleinberg, Robert, Le, Quoc V, Kleinberg, Jon

“…Deep reinforcement learning has achieved many recent successes, but our understanding of its strengths and limitations is hampered by the lack of rich…”
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