Zobrazeno 1 - 10
of 2 331
pro vyhledávání: '"Driess A"'
Autor:
Ma, Yecheng Jason, Hejna, Joey, Wahid, Ayzaan, Fu, Chuyuan, Shah, Dhruv, Liang, Jacky, Xu, Zhuo, Kirmani, Sean, Xu, Peng, Driess, Danny, Xiao, Ted, Tompson, Jonathan, Bastani, Osbert, Jayaraman, Dinesh, Yu, Wenhao, Zhang, Tingnan, Sadigh, Dorsa, Xia, Fei
Predicting temporal progress from visual trajectories is important for intelligent robots that can learn, adapt, and improve. However, learning such progress estimator, or temporal value function, across different tasks and domains requires both a la
Externí odkaz:
http://arxiv.org/abs/2411.04549
Autor:
Nasiriany, Soroush, Kirmani, Sean, Ding, Tianli, Smith, Laura, Zhu, Yuke, Driess, Danny, Sadigh, Dorsa, Xiao, Ted
We explore how intermediate policy representations can facilitate generalization by providing guidance on how to perform manipulation tasks. Existing representations such as language, goal images, and trajectory sketches have been shown to be helpful
Externí odkaz:
http://arxiv.org/abs/2411.02704
Autor:
Black, Kevin, Brown, Noah, Driess, Danny, Esmail, Adnan, Equi, Michael, Finn, Chelsea, Fusai, Niccolo, Groom, Lachy, Hausman, Karol, Ichter, Brian, Jakubczak, Szymon, Jones, Tim, Ke, Liyiming, Levine, Sergey, Li-Bell, Adrian, Mothukuri, Mohith, Nair, Suraj, Pertsch, Karl, Shi, Lucy Xiaoyang, Tanner, James, Vuong, Quan, Walling, Anna, Wang, Haohuan, Zhilinsky, Ury
Robot learning holds tremendous promise to unlock the full potential of flexible, general, and dexterous robot systems, as well as to address some of the deepest questions in artificial intelligence. However, bringing robot learning to the level of g
Externí odkaz:
http://arxiv.org/abs/2410.24164
Autor:
Zhao, Tony Z., Tompson, Jonathan, Driess, Danny, Florence, Pete, Ghasemipour, Kamyar, Finn, Chelsea, Wahid, Ayzaan
Recent work has shown promising results for learning end-to-end robot policies using imitation learning. In this work we address the question of how far can we push imitation learning for challenging dexterous manipulation tasks. We show that a simpl
Externí odkaz:
http://arxiv.org/abs/2410.13126
Autor:
Jain, Vidhi, Attarian, Maria, Joshi, Nikhil J, Wahid, Ayzaan, Driess, Danny, Vuong, Quan, Sanketi, Pannag R, Sermanet, Pierre, Welker, Stefan, Chan, Christine, Gilitschenski, Igor, Bisk, Yonatan, Dwibedi, Debidatta
Large-scale multi-task robotic manipulation systems often rely on text to specify the task. In this work, we explore whether a robot can learn by observing humans. To do so, the robot must understand a person's intent and perform the inferred task de
Externí odkaz:
http://arxiv.org/abs/2403.12943
Autor:
Nasiriany, Soroush, Xia, Fei, Yu, Wenhao, Xiao, Ted, Liang, Jacky, Dasgupta, Ishita, Xie, Annie, Driess, Danny, Wahid, Ayzaan, Xu, Zhuo, Vuong, Quan, Zhang, Tingnan, Lee, Tsang-Wei Edward, Lee, Kuang-Huei, Xu, Peng, Kirmani, Sean, Zhu, Yuke, Zeng, Andy, Hausman, Karol, Heess, Nicolas, Finn, Chelsea, Levine, Sergey, Ichter, Brian
Vision language models (VLMs) have shown impressive capabilities across a variety of tasks, from logical reasoning to visual understanding. This opens the door to richer interaction with the world, for example robotic control. However, VLMs produce o
Externí odkaz:
http://arxiv.org/abs/2402.07872
Autor:
Chen, Boyuan, Xu, Zhuo, Kirmani, Sean, Ichter, Brian, Driess, Danny, Florence, Pete, Sadigh, Dorsa, Guibas, Leonidas, Xia, Fei
Understanding and reasoning about spatial relationships is a fundamental capability for Visual Question Answering (VQA) and robotics. While Vision Language Models (VLM) have demonstrated remarkable performance in certain VQA benchmarks, they still la
Externí odkaz:
http://arxiv.org/abs/2401.12168
Autor:
Firoozi, Roya, Tucker, Johnathan, Tian, Stephen, Majumdar, Anirudha, Sun, Jiankai, Liu, Weiyu, Zhu, Yuke, Song, Shuran, Kapoor, Ashish, Hausman, Karol, Ichter, Brian, Driess, Danny, Wu, Jiajun, Lu, Cewu, Schwager, Mac
We survey applications of pretrained foundation models in robotics. Traditional deep learning models in robotics are trained on small datasets tailored for specific tasks, which limits their adaptability across diverse applications. In contrast, foun
Externí odkaz:
http://arxiv.org/abs/2312.07843
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