KALIE: Fine-Tuning Vision-Language Models for Open-World Manipulation without Robot Data

Autor: Tang, Grace, Rajkumar, Swetha, Zhou, Yifei, Walke, Homer Rich, Levine, Sergey, Fang, Kuan

Building generalist robotic systems involves effectively endowing robots with the capabilities to handle novel objects in an open-world setting. Inspired by the advances of large pre-trained models, we propose Keypoint Affordance Learning from Imagin

Externí odkaz: http://arxiv.org/abs/2409.14066

Autonomous Improvement of Instruction Following Skills via Foundation Models

Autor: Zhou, Zhiyuan, Atreya, Pranav, Lee, Abraham, Walke, Homer, Mees, Oier, Levine, Sergey

Intelligent instruction-following robots capable of improving from autonomously collected experience have the potential to transform robot learning: instead of collecting costly teleoperated demonstration data, large-scale deployment of fleets of rob

Externí odkaz: http://arxiv.org/abs/2407.20635

Protoporphyrin IX in serum of glioblastoma patients after 5-ALA administration – a potential biomarker?

Autor: A. Walke, C. Krone, S. Hellwig, M. Müther, W. Stummer, S. König, E. Suero-Molina

Publikováno v: Brain and Spine, Vol 2, Iss , Pp 101509- (2022)

Externí odkaz: https://doaj.org/article/681a8c5e2a85469db65a4fb882a83f88

Octo: An Open-Source Generalist Robot Policy

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

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset

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