Kirsty Ellis

Luca Macesanu

Matthew Leonard

Meedeum Cho

Özgür Aslan

Shivin Dass

Tony Wang

Xingfang Yuan

Abhishek Gupta

Dinesh Jayaraman

Glen Berseth … (voir 6 de plus)

Kostas Daniilidis

Roberto Martín-Martín

Youngwoon Lee

Percy Liang

Chelsea Finn

Sergey Levine

2025-08-08

robot-learning.org/CoRL/2025/Conference (présentation orale)

openreview.net

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

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

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.

2024-07-15

Robotics: Science and Systems XX (publié)

openreview.net

ConceptGraphs: Open-Vocabulary 3D Scene Graphs for Perception and Planning

Qiao Gu

Alihusein Kuwajerwala

Sacha Morin

Krishna Murthy

Bipasha Sen

Aditya Agarwal

Corban Rivera

William Paul

Rama Chellappa

Chuang Gan

Celso M de Melo

Joshua B. Tenenbaum

Antonio Torralba

Florian Shkurti

Liam Paull

For robots to perform a wide variety of tasks, they require a 3D representation of the world that is semantically rich, yet compact and effi… (voir plus)cient for task-driven perception and planning. Recent approaches have attempted to leverage features from large vision-language models to encode semantics in 3D representations. However, these approaches tend to produce maps with per-point feature vectors, which do not scale well in larger environments, nor do they contain semantic spatial relationships between entities in the environment, which are useful for downstream planning. In this work, we propose ConceptGraphs, an open-vocabulary graph-structured representation for 3D scenes. ConceptGraphs is built by leveraging 2D foundation models and fusing their output to 3D by multi-view association. The resulting representations generalize to novel semantic classes, without the need to collect large 3D datasets or finetune models. We demonstrate the utility of this representation through a number of downstream planning tasks that are specified through abstract (language) prompts and require complex reasoning over spatial and semantic concepts. (Project page: https://concept-graphs.github.io/ Explainer video: https://youtu.be/mRhNkQwRYnc )

2024-05-13

2024 IEEE International Conference on Robotics and Automation (ICRA) (publié)

openreview.net

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

Peter David Fagan

Joey Hejna

Masha Itkina

Marion Lepert

Ye Ma

Patrick Tree Miller

Jimmy Wu

Suneel Belkhale

Shivin Dass

Huy Ha … (voir 79 de plus)

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

Donovon Jackson

Charlotte Le

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

Jeannette Bohg

Ken Goldberg

Abhinav Gupta

Abhishek Gupta

Dinesh Jayaraman

Joseph J Lim

Jitendra Malik

Roberto Mart'in-Mart'in

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.

2024-03-19

ArXiv (prépublication)

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

Peter David Fagan

Joey Hejna

Masha Itkina

Marion Lepert

Ye Ma

Patrick Tree Miller

Jimmy Wu

Suneel Belkhale

Shivin Dass

Huy Ha … (voir 79 de plus)

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

Donovon Jackson

Charlotte Le

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

Jeannette Bohg

Ken Goldberg

Abhinav Gupta

Abhishek Gupta

Dinesh Jayaraman

Joseph J Lim

Jitendra Malik

Roberto Mart'in-Mart'in

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.

2024-03-19

ArXiv (prépublication)

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

Peter David Fagan

Joey Hejna

Masha Itkina

Marion Lepert

Ye Ma

Patrick Tree Miller

Jimmy Wu

Suneel Belkhale

Shivin Dass

Huy Ha … (voir 79 de plus)

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

Donovon Jackson

Charlotte Le

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

Jeannette Bohg

Ken Goldberg

Abhinav Gupta

Abhishek Gupta

Dinesh Jayaraman

Joseph J Lim

Jitendra Malik

Roberto Mart'in-Mart'in

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.

2024-03-19

ArXiv (prépublication)

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

Peter David Fagan

Joey Hejna

Masha Itkina

Marion Lepert

Ye Ma

Patrick Tree Miller

Jimmy Wu

Suneel Belkhale

Shivin Dass

Huy Ha … (voir 79 de plus)

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

Donovon Jackson

Charlotte Le

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

Jeannette Bohg

Ken Goldberg

Abhinav Gupta

Abhishek Gupta

Dinesh Jayaraman

Joseph J Lim

Jitendra Malik

Roberto Mart'in-Mart'in

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.

2024-03-19

ArXiv (prépublication)