Zobrazeno 1 - 10
of 1 395
pro vyhledávání: '"Sadigh, P"'
Autor:
Kim, Moo Jin, Pertsch, Karl, Karamcheti, Siddharth, Xiao, Ted, Balakrishna, Ashwin, Nair, Suraj, Rafailov, Rafael, Foster, Ethan, Lam, Grace, Sanketi, Pannag, Vuong, Quan, Kollar, Thomas, Burchfiel, Benjamin, Tedrake, Russ, Sadigh, Dorsa, Levine, Sergey, Liang, Percy, Finn, Chelsea
Large policies pretrained on a combination of Internet-scale vision-language data and diverse robot demonstrations have the potential to change how we teach robots new skills: rather than training new behaviors from scratch, we can fine-tune such vis
Externí odkaz:
http://arxiv.org/abs/2406.09246
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:
Octo Model Team, Ghosh, Dibya, Walke, Homer, Pertsch, Karl, Black, Kevin, Mees, Oier, Dasari, Sudeep, Hejna, Joey, Kreiman, Tobias, Xu, Charles, Luo, Jianlan, Tan, You Liang, Chen, Lawrence Yunliang, Sanketi, Pannag, Vuong, Quan, Xiao, Ted, Sadigh, Dorsa, Finn, Chelsea, Levine, Sergey
Large policies pretrained on diverse robot datasets have the potential to transform robotic learning: instead of training new policies from scratch, such generalist robot policies may be finetuned with only a little in-domain data, yet generalize bro
Externí odkaz:
http://arxiv.org/abs/2405.12213
Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and therm
Externí odkaz:
http://arxiv.org/abs/2405.06599
Modern AI systems such as self-driving cars and game-playing agents achieve superhuman performance, but often lack human-like features such as generalization, interpretability and human inter-operability. Inspired by the rich interactions between lan
Externí odkaz:
http://arxiv.org/abs/2405.04118
An accurate description of information is relevant for a range of problems in atomistic modeling, such as sampling methods, detecting rare events, analyzing datasets, or performing uncertainty quantification (UQ) in machine learning (ML)-driven simul
Externí odkaz:
http://arxiv.org/abs/2404.12367
Autor:
Ren, Allen Z., Clark, Jaden, Dixit, Anushri, Itkina, Masha, Majumdar, Anirudha, Sadigh, Dorsa
We consider the problem of Embodied Question Answering (EQA), which refers to settings where an embodied agent such as a robot needs to actively explore an environment to gather information until it is confident about the answer to a question. In thi
Externí odkaz:
http://arxiv.org/abs/2403.15941
Autor:
Khazatsky, Alexander, Pertsch, Karl, Nair, Suraj, Balakrishna, Ashwin, Dasari, Sudeep, Karamcheti, Siddharth, Nasiriany, Soroush, Srirama, Mohan Kumar, Chen, Lawrence Yunliang, Ellis, Kirsty, Fagan, Peter David, Hejna, Joey, Itkina, Masha, Lepert, Marion, Ma, Yecheng Jason, Miller, Patrick Tree, Wu, Jimmy, Belkhale, Suneel, Dass, Shivin, Ha, Huy, Jain, Arhan, Lee, Abraham, Lee, Youngwoon, Memmel, Marius, Park, Sungjae, Radosavovic, Ilija, Wang, Kaiyuan, Zhan, Albert, Black, Kevin, Chi, Cheng, Hatch, Kyle Beltran, Lin, Shan, Lu, Jingpei, Mercat, Jean, Rehman, Abdul, Sanketi, Pannag R, Sharma, Archit, Simpson, Cody, Vuong, Quan, Walke, Homer Rich, Wulfe, Blake, Xiao, Ted, Yang, Jonathan Heewon, Yavary, Arefeh, Zhao, Tony Z., Agia, Christopher, Baijal, Rohan, Castro, Mateo Guaman, Chen, Daphne, Chen, Qiuyu, Chung, Trinity, Drake, Jaimyn, Foster, Ethan Paul, Gao, Jensen, Herrera, David Antonio, Heo, Minho, Hsu, Kyle, Hu, Jiaheng, Jackson, Donovon, Le, Charlotte, Li, Yunshuang, Lin, Kevin, Lin, Roy, Ma, Zehan, Maddukuri, Abhiram, Mirchandani, Suvir, Morton, Daniel, Nguyen, Tony, O'Neill, Abigail, Scalise, Rosario, Seale, Derick, Son, Victor, Tian, Stephen, Tran, Emi, Wang, Andrew E., Wu, Yilin, Xie, Annie, Yang, Jingyun, Yin, Patrick, Zhang, Yunchu, Bastani, Osbert, Berseth, Glen, Bohg, Jeannette, Goldberg, Ken, Gupta, Abhinav, Gupta, Abhishek, Jayaraman, Dinesh, Lim, Joseph J, Malik, Jitendra, Martín-Martín, Roberto, Ramamoorthy, Subramanian, Sadigh, Dorsa, Song, Shuran, Wu, Jiajun, Yip, Michael C., Zhu, Yuke, Kollar, Thomas, Levine, Sergey, Finn, Chelsea
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and robust robotic manipulation policies. However, creating such datasets is challenging: collecting robot manipul
Externí odkaz:
http://arxiv.org/abs/2403.12945
Data collection has become an increasingly important problem in robotic manipulation, yet there still lacks much understanding of how to effectively collect data to facilitate broad generalization. Recent works on large-scale robotic data collection
Externí odkaz:
http://arxiv.org/abs/2403.05110
Autor:
Sundaresan, Priya, Vuong, Quan, Gu, Jiayuan, Xu, Peng, Xiao, Ted, Kirmani, Sean, Yu, Tianhe, Stark, Michael, Jain, Ajinkya, Hausman, Karol, Sadigh, Dorsa, Bohg, Jeannette, Schaal, Stefan
Natural language and images are commonly used as goal representations in goal-conditioned imitation learning (IL). However, natural language can be ambiguous and images can be over-specified. In this work, we propose hand-drawn sketches as a modality
Externí odkaz:
http://arxiv.org/abs/2403.02709