Zobrazeno 1 - 10
of 84
pro vyhledávání: '"Ahn, Michael"'
Autor:
Ahn, Michael, Arenas, Montserrat Gonzalez, Bennice, Matthew, Brown, Noah, Chan, Christine, David, Byron, Francis, Anthony, Gonzalez, Gavin, Hessmer, Rainer, Jackson, Tomas, Joshi, Nikhil J, Lam, Daniel, Lee, Tsang-Wei Edward, Luong, Alex, Maddineni, Sharath, Patel, Harsh, Peralta, Jodilyn, Quiambao, Jornell, Reyes, Diego, Ruano, Rosario M Jauregui, Sadigh, Dorsa, Sanketi, Pannag, Takayama, Leila, Vodenski, Pavel, Xia, Fei
Robots today can exploit the rich world knowledge of large language models to chain simple behavioral skills into long-horizon tasks. However, robots often get interrupted during long-horizon tasks due to primitive skill failures and dynamic environm
Externí odkaz:
http://arxiv.org/abs/2405.16021
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:
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:
D'Ambrosio, David B., Abelian, Jonathan, Abeyruwan, Saminda, Ahn, Michael, Bewley, Alex, Boyd, Justin, Choromanski, Krzysztof, Cortes, Omar, Coumans, Erwin, Ding, Tianli, Gao, Wenbo, Graesser, Laura, Iscen, Atil, Jaitly, Navdeep, Jain, Deepali, Kangaspunta, Juhana, Kataoka, Satoshi, Kouretas, Gus, Kuang, Yuheng, Lazic, Nevena, Lynch, Corey, Mahjourian, Reza, Moore, Sherry Q., Nguyen, Thinh, Oslund, Ken, Reed, Barney J, Reymann, Krista, Sanketi, Pannag R., Shankar, Anish, Sermanet, Pierre, Sindhwani, Vikas, Singh, Avi, Vanhoucke, Vincent, Vesom, Grace, Xu, Peng
We present a deep-dive into a real-world robotic learning system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts to
Externí odkaz:
http://arxiv.org/abs/2309.03315
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
Reinforcement learning provides a general framework for learning robotic skills while minimizing engineering effort. However, most reinforcement learning algorithms assume that a well-designed reward function is provided, and learn a single behavior
Externí odkaz:
http://arxiv.org/abs/2004.12974
Autor:
Ahn, Michael, Zhu, Henry, Hartikainen, Kristian, Ponte, Hugo, Gupta, Abhishek, Levine, Sergey, Kumar, Vikash
Publikováno v:
Conference on Robot Learning, 2019
ROBEL is an open-source platform of cost-effective robots designed for reinforcement learning in the real world. ROBEL introduces two robots, each aimed to accelerate reinforcement learning research in different task domains: D'Claw is a three-finger
Externí odkaz:
http://arxiv.org/abs/1909.11639
Manipulation and locomotion are closely related problems that are often studied in isolation. In this work, we study the problem of coordinating multiple mobile agents to exhibit manipulation behaviors using a reinforcement learning (RL) approach. Ou
Externí odkaz:
http://arxiv.org/abs/1908.05224
Autor:
Ahn, Michael J., Chen, Yu-Che
Publikováno v:
In Government Information Quarterly April 2022 39(2)
Autor:
Ahn, Michael Ji-Sung.
Publikováno v:
Related electronic resource: Current Research at SU : database of SU dissertations, recent titles available, full text.
Thesis (Ph.D.)--Syracuse University, 2007.
"Publication number: AAT 3281715."
"Publication number: AAT 3281715."
Externí odkaz:
http://wwwlib.umi.com/cr/syr/main