Zobrazeno 1 - 10
of 13 340
pro vyhledávání: '"A. Balakrishna"'
The width of the magnetic hysteresis loop is often correlated with the material's magnetocrystalline anisotropy constant $\kappa_1$. Traditionally, a common approach to reduce the hysteresis width has been to develop alloys with $\kappa_1$ as close t
Externí odkaz:
http://arxiv.org/abs/2411.12875
We address a moving boundary problem that consists of a system of equations modeling an inviscid fluid interacting with a two-dimensional nonlinear Koiter plate at the boundary. We derive a priori estimates needed to prove the local-in-time existence
Externí odkaz:
http://arxiv.org/abs/2411.00115
Autor:
Hatch, Kyle B., Balakrishna, Ashwin, Mees, Oier, Nair, Suraj, Park, Seohong, Wulfe, Blake, Itkina, Masha, Eysenbach, Benjamin, Levine, Sergey, Kollar, Thomas, Burchfiel, Benjamin
Image and video generative models that are pre-trained on Internet-scale data can greatly increase the generalization capacity of robot learning systems. These models can function as high-level planners, generating intermediate subgoals for low-level
Externí odkaz:
http://arxiv.org/abs/2410.20018
This paper introduces an integer-valued generalized autoregressive conditional heteroskedasticity (INGARCH) model based on the novel geometric distribution and discusses some of its properties. The parameter estimation problem of the models are studi
Externí odkaz:
http://arxiv.org/abs/2410.01283
Autor:
Yu, Justin, Hari, Kush, Srinivas, Kishore, El-Refai, Karim, Rashid, Adam, Kim, Chung Min, Kerr, Justin, Cheng, Richard, Irshad, Muhammad Zubair, Balakrishna, Ashwin, Kollar, Thomas, Goldberg, Ken
Building semantic 3D maps is valuable for searching for objects of interest in offices, warehouses, stores, and homes. We present a mapping system that incrementally builds a Language-Embedded Gaussian Splat (LEGS): a detailed 3D scene representation
Externí odkaz:
http://arxiv.org/abs/2409.18108
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:
Booher, Jonathan, Rohanimanesh, Khashayar, Xu, Junhong, Isenbaev, Vladislav, Balakrishna, Ashwin, Gupta, Ishan, Liu, Wei, Petiushko, Aleksandr
Modern approaches to autonomous driving rely heavily on learned components trained with large amounts of human driving data via imitation learning. However, these methods require large amounts of expensive data collection and even then face challenge
Externí odkaz:
http://arxiv.org/abs/2406.08878
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
Utilizing the well-established Radial Tully-Fisher (RTF) relation observed in a `large' (843) sample of local galaxies, we report the maximum allowed variance in the Hubble parameter, $H_0$. We estimate the total intrinsic scatter in the magnitude of
Externí odkaz:
http://arxiv.org/abs/2403.06859
This article focuses on the coherent forecasting of the recently introduced novel geometric AR(1) (NoGeAR(1)) model - an INAR model based on inflated - parameter binomial thinning approach. Various techniques are available to achieve h - step ahead c
Externí odkaz:
http://arxiv.org/abs/2403.00304