Zobrazeno 1 - 10
of 260
pro vyhledávání: '"Sadigh, Dorsa"'
Autor:
Jenamani, Rajat Kumar, Sundaresan, Priya, Sakr, Maram, Bhattacharjee, Tapomayukh, Sadigh, Dorsa
Robot-assisted feeding has the potential to improve the quality of life for individuals with mobility limitations who are unable to feed themselves independently. However, there exists a large gap between the homogeneous, curated plates existing feed
Externí odkaz:
http://arxiv.org/abs/2407.07561
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
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
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
Autor:
Belkhale, Suneel, Ding, Tianli, Xiao, Ted, Sermanet, Pierre, Vuong, Quon, Tompson, Jonathan, Chebotar, Yevgen, Dwibedi, Debidatta, Sadigh, Dorsa
Language provides a way to break down complex concepts into digestible pieces. Recent works in robot imitation learning use language-conditioned policies that predict actions given visual observations and the high-level task specified in language. Th
Externí odkaz:
http://arxiv.org/abs/2403.01823