Zobrazeno 1 - 10
of 509
pro vyhledávání: '"Julian, Ryan"'
Autor:
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 useful tasks. However, one of the key challenges of training embodied foundation models is t
Externí odkaz:
http://arxiv.org/abs/2401.12963
This paper combines two contributions. First, we introduce an extension of the Meta-World benchmark, which we call "Language-World," which allows a large language model to operate in a simulated robotic environment using semi-structured natural langu
Externí odkaz:
http://arxiv.org/abs/2310.17019
Autor:
Collaboration, Open X-Embodiment, O'Neill, Abby, Rehman, Abdul, Gupta, Abhinav, Maddukuri, Abhiram, Gupta, Abhishek, Padalkar, Abhishek, Lee, Abraham, Pooley, Acorn, Gupta, Agrim, Mandlekar, Ajay, Jain, Ajinkya, Tung, Albert, Bewley, Alex, Herzog, Alex, Irpan, Alex, Khazatsky, Alexander, Rai, Anant, Gupta, Anchit, Wang, Andrew, Kolobov, Andrey, Singh, Anikait, Garg, Animesh, Kembhavi, Aniruddha, Xie, Annie, Brohan, Anthony, Raffin, Antonin, Sharma, Archit, Yavary, Arefeh, Jain, Arhan, Balakrishna, Ashwin, Wahid, Ayzaan, Burgess-Limerick, Ben, Kim, Beomjoon, Schölkopf, Bernhard, Wulfe, Blake, Ichter, Brian, Lu, Cewu, Xu, Charles, Le, Charlotte, Finn, Chelsea, Wang, Chen, Xu, Chenfeng, Chi, Cheng, Huang, Chenguang, Chan, Christine, Agia, Christopher, Pan, Chuer, Fu, Chuyuan, Devin, Coline, Xu, Danfei, Morton, Daniel, Driess, Danny, Chen, Daphne, Pathak, Deepak, Shah, Dhruv, Büchler, Dieter, Jayaraman, Dinesh, Kalashnikov, Dmitry, Sadigh, Dorsa, Johns, Edward, Foster, Ethan, Liu, Fangchen, Ceola, Federico, Xia, Fei, Zhao, Feiyu, Frujeri, Felipe Vieira, Stulp, Freek, Zhou, Gaoyue, Sukhatme, Gaurav S., Salhotra, Gautam, Yan, Ge, Feng, Gilbert, Schiavi, Giulio, Berseth, Glen, Kahn, Gregory, Yang, Guangwen, Wang, Guanzhi, Su, Hao, Fang, Hao-Shu, Shi, Haochen, Bao, Henghui, Amor, Heni Ben, Christensen, Henrik I, Furuta, Hiroki, Bharadhwaj, Homanga, Walke, Homer, Fang, Hongjie, Ha, Huy, Mordatch, Igor, Radosavovic, Ilija, Leal, Isabel, Liang, Jacky, Abou-Chakra, Jad, Kim, Jaehyung, Drake, Jaimyn, Peters, Jan, Schneider, Jan, Hsu, Jasmine, Vakil, Jay, Bohg, Jeannette, Bingham, Jeffrey, Wu, Jeffrey, Gao, Jensen, Hu, Jiaheng, Wu, Jiajun, Wu, Jialin, Sun, Jiankai, Luo, Jianlan, Gu, Jiayuan, Tan, Jie, Oh, Jihoon, Wu, Jimmy, Lu, Jingpei, Yang, Jingyun, Malik, Jitendra, Silvério, João, Hejna, Joey, Booher, Jonathan, Tompson, Jonathan, Yang, Jonathan, Salvador, Jordi, Lim, Joseph J., Han, Junhyek, Wang, Kaiyuan, Rao, Kanishka, Pertsch, Karl, Hausman, Karol, Go, Keegan, Gopalakrishnan, Keerthana, Goldberg, Ken, Byrne, Kendra, Oslund, Kenneth, Kawaharazuka, Kento, Black, Kevin, Lin, Kevin, Zhang, Kevin, Ehsani, Kiana, Lekkala, Kiran, Ellis, Kirsty, Rana, Krishan, Srinivasan, Krishnan, Fang, Kuan, Singh, Kunal Pratap, Zeng, Kuo-Hao, Hatch, Kyle, Hsu, Kyle, Itti, Laurent, Chen, Lawrence Yunliang, Pinto, Lerrel, Fei-Fei, Li, Tan, Liam, Fan, Linxi "Jim", Ott, Lionel, Lee, Lisa, Weihs, Luca, Chen, Magnum, Lepert, Marion, Memmel, Marius, Tomizuka, Masayoshi, Itkina, Masha, Castro, Mateo Guaman, Spero, Max, Du, Maximilian, Ahn, Michael, Yip, Michael C., Zhang, Mingtong, Ding, Mingyu, Heo, Minho, Srirama, Mohan Kumar, Sharma, Mohit, Kim, Moo Jin, Kanazawa, Naoaki, Hansen, Nicklas, Heess, Nicolas, Joshi, Nikhil J, Suenderhauf, Niko, Liu, Ning, Di Palo, Norman, Shafiullah, Nur Muhammad Mahi, Mees, Oier, Kroemer, Oliver, Bastani, Osbert, Sanketi, Pannag R, Miller, Patrick "Tree", Yin, Patrick, Wohlhart, Paul, Xu, Peng, Fagan, Peter David, Mitrano, Peter, Sermanet, Pierre, Abbeel, Pieter, Sundaresan, Priya, Chen, Qiuyu, Vuong, Quan, Rafailov, Rafael, Tian, Ran, Doshi, Ria, Mart'in-Mart'in, Roberto, Baijal, Rohan, Scalise, Rosario, Hendrix, Rose, Lin, Roy, Qian, Runjia, Zhang, Ruohan, Mendonca, Russell, Shah, Rutav, Hoque, Ryan, Julian, Ryan, Bustamante, Samuel, Kirmani, Sean, Levine, Sergey, Lin, Shan, Moore, Sherry, Bahl, Shikhar, Dass, Shivin, Sonawani, Shubham, Tulsiani, Shubham, Song, Shuran, Xu, Sichun, Haldar, Siddhant, Karamcheti, Siddharth, Adebola, Simeon, Guist, Simon, Nasiriany, Soroush, Schaal, Stefan, Welker, Stefan, Tian, Stephen, Ramamoorthy, Subramanian, Dasari, Sudeep, Belkhale, Suneel, Park, Sungjae, Nair, Suraj, Mirchandani, Suvir, Osa, Takayuki, Gupta, Tanmay, Harada, Tatsuya, Matsushima, Tatsuya, Xiao, Ted, Kollar, Thomas, Yu, Tianhe, Ding, Tianli, Davchev, Todor, Zhao, Tony Z., Armstrong, Travis, Darrell, Trevor, Chung, Trinity, Jain, Vidhi, Kumar, Vikash, Vanhoucke, Vincent, Zhan, Wei, Zhou, Wenxuan, Burgard, Wolfram, Chen, Xi, Chen, Xiangyu, Wang, Xiaolong, Zhu, Xinghao, Geng, Xinyang, Liu, Xiyuan, Liangwei, Xu, Li, Xuanlin, Pang, Yansong, Lu, Yao, Ma, Yecheng Jason, Kim, Yejin, Chebotar, Yevgen, Zhou, Yifan, Zhu, Yifeng, Wu, Yilin, Xu, Ying, Wang, Yixuan, Bisk, Yonatan, Dou, Yongqiang, Cho, Yoonyoung, Lee, Youngwoon, Cui, Yuchen, Cao, Yue, Wu, Yueh-Hua, Tang, Yujin, Zhu, Yuke, Zhang, Yunchu, Jiang, Yunfan, Li, Yunshuang, Li, Yunzhu, Iwasawa, Yusuke, Matsuo, Yutaka, Ma, Zehan, Xu, Zhuo, Cui, Zichen Jeff, Zhang, Zichen, Fu, Zipeng, Lin, Zipeng
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretra
Externí odkaz:
http://arxiv.org/abs/2310.08864
Autor:
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 enable emergent semantic reasoning. Our goal is to enable a single end-to-end trained model to
Externí odkaz:
http://arxiv.org/abs/2307.15818
Autor:
Herzog, Alexander, Rao, Kanishka, Hausman, Karol, Lu, Yao, Wohlhart, Paul, Yan, Mengyuan, Lin, Jessica, Arenas, Montserrat Gonzalez, Xiao, Ted, Kappler, Daniel, Ho, Daniel, Rettinghouse, Jarek, Chebotar, Yevgen, Lee, Kuang-Huei, Gopalakrishnan, Keerthana, Julian, Ryan, Li, Adrian, Fu, Chuyuan Kelly, Wei, Bob, Ramesh, Sangeetha, Holden, Khem, Kleiven, Kim, Rendleman, David, Kirmani, Sean, Bingham, Jeff, Weisz, Jon, Xu, Ying, Lu, Wenlong, Bennice, Matthew, Fong, Cody, Do, David, Lam, Jessica, Bai, Yunfei, Holson, Benjie, Quinlan, Michael, Brown, Noah, Kalakrishnan, Mrinal, Ibarz, Julian, Pastor, Peter, Levine, Sergey
We describe a system for deep reinforcement learning of robotic manipulation skills applied to a large-scale real-world task: sorting recyclables and trash in office buildings. Real-world deployment of deep RL policies requires not only effective tra
Externí odkaz:
http://arxiv.org/abs/2305.03270
Autor:
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 with small task-specific datasets to a high level of performance. While this capability has
Externí odkaz:
http://arxiv.org/abs/2212.06817
Autor:
Ahn, Michael, Brohan, Anthony, Brown, Noah, Chebotar, Yevgen, Cortes, Omar, David, Byron, Finn, Chelsea, Fu, Chuyuan, Gopalakrishnan, Keerthana, Hausman, Karol, Herzog, Alex, Ho, Daniel, Hsu, Jasmine, Ibarz, Julian, Ichter, Brian, Irpan, Alex, Jang, Eric, Ruano, Rosario Jauregui, Jeffrey, Kyle, Jesmonth, Sally, Joshi, Nikhil J, Julian, Ryan, Kalashnikov, Dmitry, Kuang, Yuheng, Lee, Kuang-Huei, Levine, Sergey, Lu, Yao, Luu, Linda, Parada, Carolina, Pastor, Peter, Quiambao, Jornell, Rao, Kanishka, Rettinghouse, Jarek, Reyes, Diego, Sermanet, Pierre, Sievers, Nicolas, Tan, Clayton, Toshev, Alexander, Vanhoucke, Vincent, Xia, Fei, Xiao, Ted, Xu, Peng, Xu, Sichun, Yan, Mengyuan, Zeng, Andy
Large language models can encode a wealth of semantic knowledge about the world. Such knowledge could be extremely useful to robots aiming to act upon high-level, temporally extended instructions expressed in natural language. However, a significant
Externí odkaz:
http://arxiv.org/abs/2204.01691
In order to be effective general purpose machines in real world environments, robots not only will need to adapt their existing manipulation skills to new circumstances, they will need to acquire entirely new skills on-the-fly. A great promise of con
Externí odkaz:
http://arxiv.org/abs/2110.10255
We explore possible methods for multi-task transfer learning which seek to exploit the shared physical structure of robotics tasks. Specifically, we train policies for a base set of pre-training tasks, then experiment with adapting to new off-distrib
Externí odkaz:
http://arxiv.org/abs/2106.13237
Autor:
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 online exploration, a setting that is becoming increasingly important for scaling robot learni
Externí odkaz:
http://arxiv.org/abs/2104.07749