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pro vyhledávání: '"Gupta, Abhinav"'
Autor:
Bharadhwaj, Homanga, Dwibedi, Debidatta, Gupta, Abhinav, Tulsiani, Shubham, Doersch, Carl, Xiao, Ted, Shah, Dhruv, Xia, Fei, Sadigh, Dorsa, Kirmani, Sean
How can robot manipulation policies generalize to novel tasks involving unseen object types and new motions? In this paper, we provide a solution in terms of predicting motion information from web data through human video generation and conditioning
Externí odkaz:
http://arxiv.org/abs/2409.16283
Performing long-term experimentation or large-scale data collection for machine learning in the field of soft robotics is challenging, due to the hardware robustness and experimental flexibility required. In this work, we propose a modular parallel r
Externí odkaz:
http://arxiv.org/abs/2409.03614
In order to *generalize* to various tasks in the wild, robotic agents will need a suitable representation (i.e., vision network) that enables the robot to predict optimal actions given high dimensional vision inputs. However, learning such a represen
Externí odkaz:
http://arxiv.org/abs/2407.18911
Masked Image Modeling (MIM) has emerged as a promising method for deriving visual representations from unlabeled image data by predicting missing pixels from masked portions of images. It excels in region-aware learning and provides strong initializa
Externí odkaz:
http://arxiv.org/abs/2407.15837
Although pre-training on a large amount of data is beneficial for robot learning, current paradigms only perform large-scale pretraining for visual representations, whereas representations for other modalities are trained from scratch. In contrast to
Externí odkaz:
http://arxiv.org/abs/2405.08576
We seek to learn a generalizable goal-conditioned policy that enables zero-shot robot manipulation: interacting with unseen objects in novel scenes without test-time adaptation. While typical approaches rely on a large amount of demonstration data fo
Externí odkaz:
http://arxiv.org/abs/2405.01527
Autor:
Gupta, Abhinav, Bartos, Radim
HTTP/3, the latest evolution of the Hypertext Transfer Protocol, utilizes QUIC, a new transport protocol leveraging UDP to overcome limitations such as connection time and head-of-line blocking prevalent in HTTP/2. This advancement is enhanced by the
Externí odkaz:
http://arxiv.org/abs/2404.17439
Autor:
Gupta, Abhinav, Bartos, Radim
HTTP/3 marks a significant advancement in protocol development, utilizing QUIC as its underlying transport layer to exploit multiplexing capabilities and minimize head-of-line blocking. The introduction of the Extensible Prioritization Scheme (EPS) o
Externí odkaz:
http://arxiv.org/abs/2404.13460
We propose G-HOP, a denoising diffusion based generative prior for hand-object interactions that allows modeling both the 3D object and a human hand, conditioned on the object category. To learn a 3D spatial diffusion model that can capture this join
Externí odkaz:
http://arxiv.org/abs/2404.12383
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