Copertina

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

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	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, Guizilini, Vitor, Herrera, David Antonio, Heo, Minho, Hsu, Kyle, Hu, Jiaheng, Irshad, Muhammad Zubair, 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
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Robotics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

_version_	1866915252692582400
author	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 Guizilini, Vitor Herrera, David Antonio Heo, Minho Hsu, Kyle Hu, Jiaheng Irshad, Muhammad Zubair 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
author_facet	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 Guizilini, Vitor Herrera, David Antonio Heo, Minho Hsu, Kyle Hu, Jiaheng Irshad, Muhammad Zubair 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
contents	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 manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are mostly trained on data collected in a small number of environments with limited scene and task diversity. In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset with 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup.
format	Preprint
id	arxiv_https___arxiv_org_abs_2403_12945
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset 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 Guizilini, Vitor Herrera, David Antonio Heo, Minho Hsu, Kyle Hu, Jiaheng Irshad, Muhammad Zubair 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 Robotics 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 manipulation data in diverse environments poses logistical and safety challenges and requires substantial investments in hardware and human labour. As a result, even the most general robot manipulation policies today are mostly trained on data collected in a small number of environments with limited scene and task diversity. In this work, we introduce DROID (Distributed Robot Interaction Dataset), a diverse robot manipulation dataset with 76k demonstration trajectories or 350 hours of interaction data, collected across 564 scenes and 84 tasks by 50 data collectors in North America, Asia, and Europe over the course of 12 months. We demonstrate that training with DROID leads to policies with higher performance and improved generalization ability. We open source the full dataset, policy learning code, and a detailed guide for reproducing our robot hardware setup.
title	DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
topic	Robotics
url	https://arxiv.org/abs/2403.12945