Portrait de Albert Zhan n'est pas disponible

Albert Zhan

albert.zhan@mila.quebec
Doctorat - UdeM
Superviseur⋅e principal⋅e
Glen Berseth
Sujets de recherche
Apprentissage par renforcement

Publications

DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset
Alexander Khazatsky
Karl Pertsch
Suraj Nair
Ashwin Balakrishna
Sudeep Dasari
Siddharth Karamcheti
Soroush Nasiriany
Mohan Kumar Srirama
Lawrence Yunliang Chen
Kirsty Ellis
Peter David Fagan
Joey Hejna
Masha Itkina
Marion Lepert
Yecheng Jason Ma
Ye Ma
Patrick Tree Miller
Jimmy Wu
Suneel Belkhale
Shivin Dass … (voir 82 de plus)
Huy Ha
Arhan Jain
Abraham Lee
Youngwoon Lee
Marius Memmel
Sungjae Park
Ilija Radosavovic
Kaiyuan Wang
Albert Zhan
Kevin Black
Cheng Chi
Kyle Beltran Hatch
Shan Lin
Jingpei Lu
Jean Mercat
Abdul Rehman
Pannag R Sanketi
Archit Sharma
Cody Simpson
Quan Vuong
Homer Rich Walke
Blake Wulfe
Ted Xiao
Jonathan Heewon Yang
Arefeh Yavary
Tony Z. Zhao
Christopher Agia
Rohan Baijal
Mateo Guaman Castro
Daphne Chen
Qiuyu Chen
Trinity Chung
Jaimyn Drake
Ethan Paul Foster
Jensen Gao
David Antonio Herrera
Minho Heo
Kyle Hsu
Jiaheng Hu
Muhammad Zubair Irshad
Donovon Jackson
Charlotte Le
Xinyu Lin
Yunshuang Li
K. Lin
Roy Lin
Zehan Ma
Abhiram Maddukuri
Suvir Mirchandani
Daniel Morton
Tony Khuong Nguyen
Abigail O'Neill
Rosario Scalise
Derick Seale
Victor Son
Stephen Tian
Emi Tran
Andrew E. Wang
Yilin Wu
Annie Xie
Jingyun Yang
Patrick Yin
Yunchu Zhang
Osbert Bastani
Glen Berseth
Jeannette Bohg
Ken Goldberg
Abhinav Gupta
Abhishek Gupta
Dinesh Jayaraman
Joseph J Lim
Jitendra Malik
Roberto Martín-Martín
Subramanian Ramamoorthy
Dorsa Sadigh
Shuran Song
Jiajun Wu
Michael C. Yip
Yuke Zhu
Thomas Kollar
Sergey Levine
Chelsea Finn
The creation of large, diverse, high-quality robot manipulation datasets is an important stepping stone on the path toward more capable and … (voir plus)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.
2024-07-14
Robotics: Science and Systems XX (publié)
doi.org
openreview.net