Zobrazeno 1 - 10
of 3 318
pro vyhledávání: '"A. Tompson"'
We discuss some consistent issues on how RepNet has been evaluated in various papers. As a way to mitigate these issues, we report RepNet performance results on different datasets, and release evaluation code and the RepNet checkpoint to obtain these
Externí odkaz:
http://arxiv.org/abs/2411.08878
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:
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
We introduce a dataset of annotations of temporal repetitions in videos. The dataset, OVR (pronounced as over), contains annotations for over 72K videos, with each annotation specifying the number of repetitions, the start and end time of the repetit
Externí odkaz:
http://arxiv.org/abs/2407.17085
We introduce a versatile $\textit{flexible-captioning}$ vision-language model (VLM) capable of generating region-specific descriptions of varying lengths. The model, FlexCap, is trained to produce length-conditioned captions for input bounding boxes,
Externí odkaz:
http://arxiv.org/abs/2403.12026
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
Autor:
Liang, Jacky, Xia, Fei, Yu, Wenhao, Zeng, Andy, Arenas, Montserrat Gonzalez, Attarian, Maria, Bauza, Maria, Bennice, Matthew, Bewley, Alex, Dostmohamed, Adil, Fu, Chuyuan Kelly, Gileadi, Nimrod, Giustina, Marissa, Gopalakrishnan, Keerthana, Hasenclever, Leonard, Humplik, Jan, Hsu, Jasmine, Joshi, Nikhil, Jyenis, Ben, Kew, Chase, Kirmani, Sean, Lee, Tsang-Wei Edward, Lee, Kuang-Huei, Michaely, Assaf Hurwitz, Moore, Joss, Oslund, Ken, Rao, Dushyant, Ren, Allen, Tabanpour, Baruch, Vuong, Quan, Wahid, Ayzaan, Xiao, Ted, Xu, Ying, Zhuang, Vincent, Xu, Peng, Frey, Erik, Caluwaerts, Ken, Zhang, Tingnan, Ichter, Brian, Tompson, Jonathan, Takayama, Leila, Vanhoucke, Vincent, Shafran, Izhak, Mataric, Maja, Sadigh, Dorsa, Heess, Nicolas, Rao, Kanishka, Stewart, Nik, Tan, Jie, Parada, Carolina
Large language models (LLMs) have been shown to exhibit a wide range of capabilities, such as writing robot code from language commands -- enabling non-experts to direct robot behaviors, modify them based on feedback, or compose them to perform new t
Externí odkaz:
http://arxiv.org/abs/2402.11450
Autor:
ALOHA 2 Team, Aldaco, Jorge, Armstrong, Travis, Baruch, Robert, Bingham, Jeff, Chan, Sanky, Draper, Kenneth, Dwibedi, Debidatta, Finn, Chelsea, Florence, Pete, Goodrich, Spencer, Gramlich, Wayne, Hage, Torr, Herzog, Alexander, Hoech, Jonathan, Nguyen, Thinh, Storz, Ian, Tabanpour, Baruch, Takayama, Leila, Tompson, Jonathan, Wahid, Ayzaan, Wahrburg, Ted, Xu, Sichun, Yaroshenko, Sergey, Zakka, Kevin, Zhao, Tony Z.
Diverse demonstration datasets have powered significant advances in robot learning, but the dexterity and scale of such data can be limited by the hardware cost, the hardware robustness, and the ease of teleoperation. We introduce ALOHA 2, an enhance
Externí odkaz:
http://arxiv.org/abs/2405.02292
Autor:
Attarian, Maria, Asif, Muhammad Adil, Liu, Jingzhou, Hari, Ruthrash, Garg, Animesh, Gilitschenski, Igor, Tompson, Jonathan
Publikováno v:
7th Annual Conference on Robot Learning, 2023
Many existing learning-based grasping approaches concentrate on a single embodiment, provide limited generalization to higher DoF end-effectors and cannot capture a diverse set of grasp modes. We tackle the problem of grasping using multiple embodime
Externí odkaz:
http://arxiv.org/abs/2312.03864
Autor:
Du, Yilun, Yang, Mengjiao, Florence, Pete, Xia, Fei, Wahid, Ayzaan, Ichter, Brian, Sermanet, Pierre, Yu, Tianhe, Abbeel, Pieter, Tenenbaum, Joshua B., Kaelbling, Leslie, Zeng, Andy, Tompson, Jonathan
We are interested in enabling visual planning for complex long-horizon tasks in the space of generated videos and language, leveraging recent advances in large generative models pretrained on Internet-scale data. To this end, we present video languag
Externí odkaz:
http://arxiv.org/abs/2310.10625