Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Rao, Kanishka"'
Autor:
Liang, Jacky, Xia, Fei, Yu, Wenhao, Zeng, Andy, Arenas, Montserrat Gonzalez, Attarian, Maria, Bauza, Maria, Bennice, Matthew, Bewley, Alex, Dostmohamed, Adil, Fu, Chuyuan Kelly, Gileadi, Nimrod, Giustina, Marissa, Gopalakrishnan, Keerthana, Hasenclever, Leonard, Humplik, Jan, Hsu, Jasmine, Joshi, Nikhil, Jyenis, Ben, Kew, Chase, Kirmani, Sean, Lee, Tsang-Wei Edward, Lee, Kuang-Huei, Michaely, Assaf Hurwitz, Moore, Joss, Oslund, Ken, Rao, Dushyant, Ren, Allen, Tabanpour, Baruch, Vuong, Quan, Wahid, Ayzaan, Xiao, Ted, Xu, Ying, Zhuang, Vincent, Xu, Peng, Frey, Erik, Caluwaerts, Ken, Zhang, Tingnan, Ichter, Brian, Tompson, Jonathan, Takayama, Leila, Vanhoucke, Vincent, Shafran, Izhak, Mataric, Maja, Sadigh, Dorsa, Heess, Nicolas, Rao, Kanishka, Stewart, Nik, Tan, Jie, Parada, Carolina
Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot behaviors, modify them based on feedback, or compose them to perform new t
Externí odkaz:
http://arxiv.org/abs/2402.11450
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
Autor:
Leal, Isabel, Choromanski, Krzysztof, Jain, Deepali, Dubey, Avinava, Varley, Jake, Ryoo, Michael, Lu, Yao, Liu, Frederick, Sindhwani, Vikas, Vuong, Quan, Sarlos, Tamas, Oslund, Ken, Hausman, Karol, Rao, Kanishka
We present Self-Adaptive Robust Attention for Robotics Transformers (SARA-RT): a new paradigm for addressing the emerging challenge of scaling up Robotics Transformers (RT) for on-robot deployment. SARA-RT relies on the new method of fine-tuning prop
Externí odkaz:
http://arxiv.org/abs/2312.01990
Autor:
Gu, Jiayuan, Kirmani, Sean, Wohlhart, Paul, Lu, Yao, Arenas, Montserrat Gonzalez, Rao, Kanishka, Yu, Wenhao, Fu, Chuyuan, Gopalakrishnan, Keerthana, Xu, Zhuo, Sundaresan, Priya, Xu, Peng, Su, Hao, Hausman, Karol, Finn, Chelsea, Vuong, Quan, Xiao, Ted
Generalization remains one of the most important desiderata for robust robot learning systems. While recently proposed approaches show promise in generalization to novel objects, semantic concepts, or visual distribution shifts, generalization to new
Externí odkaz:
http://arxiv.org/abs/2311.01977
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:
Abourayya, Amr, Kleesiek, Jens, Rao, Kanishka, Ayday, Erman, Rao, Bharat, Webb, Geoff, Kamp, Michael
In many critical applications, sensitive data is inherently distributed and cannot be centralized due to privacy concerns. A wide range of federated learning approaches have been proposed in the literature to train models locally at each client witho
Externí odkaz:
http://arxiv.org/abs/2310.05696
Autor:
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 demonstrations and autonomously collected data. Our method uses a Transformer to provide a sca
Externí odkaz:
http://arxiv.org/abs/2309.10150
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:
Mirchandani, Suvir, Xia, Fei, Florence, Pete, Ichter, Brian, Driess, Danny, Arenas, Montserrat Gonzalez, Rao, Kanishka, Sadigh, Dorsa, Zeng, Andy
We observe that pre-trained large language models (LLMs) are capable of autoregressively completing complex token sequences -- from arbitrary ones procedurally generated by probabilistic context-free grammars (PCFG), to more rich spatial patterns fou
Externí odkaz:
http://arxiv.org/abs/2307.04721
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