RoboTurk - Crowdsourcing Robotics
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[August 2021]
robomimic
– We released robomimic, a framework for robot learning from demonstration. It offers a broad set of demonstration datasets collected on robot manipulation domains with RoboTurk, and learning algorithms to learn from these datasets.
[May 2021]
Multi-Arm RoboTurk
– MART was nominated as a best multi-robot systems paper finalist at ICRA 2021!
[December 2020]
Human-in-the-Loop Imitation Learning
– We recently extended RoboTurk to enable human-in-the-loop teleoperation and developed Intervention Weighted Regression, a simple and effective algorithm to learn from such interventions.
System Features
6-DoF Intuitive User Interface
We provide 6 degree of freedom intuitive motion control which maps phone movement to robot arm movement.
Simultaneous Users
RoboTurk can host multiple simultaenous users that each control a robot arm in its own workspace, as well as multiple users that control robot arms in a shared workspace, allowing for demonstrations on collaborative and adversarial tasks.
Worldwide Low-Latency Robot Teleoperation
Real-time robot control of simulated and physical robot arms from across the world. This has been stress-tested by controlling robot arms at Stanford from far locations such as China and India.
Human-in-the-Loop Intervention Mechanism
Users can watch an autonomous robot arm try to solve tasks and provide assistance when necessary, helping the robot learn from its mistakes.
Datasets
Roboturk Pilot Dataset
For our first paper (CoRL 2018), we collected 1000+ successful demonstrations on each of two challenging manipulation tasks.
Click to read more and download the dataset.
137.5 hrs
robot demonstrations
22 hrs
data collection
1071
successful Picking demos
1147
successful Assembly demos
3224
total demonstrations
Roboturk Real-World Dataset
In our second paper (IROS 2019, Best Cognitive Robotics Paper Finalist), we collected over 100 hours of data, resulting in one of the largest robot datasets collected via human teleoperation, on three challenging long-horizon tasks.
Click to read more and download the dataset
111 hrs
robot demonstrations
1 week
data collection
dexterous manipulation tasks
54
non-expert users
2144
demonstrations
robomimic v0.1
We used RoboTurk to collect a suite of demonstration datasets across several simulated and real world tasks and released the datasets along with a framework to learn from such demonstration datasets
Click to read more and download the dataset
simulation tasks
real-world tasks
humans of varying proficiency
2500+
human trajectories
Core System Projects
Projects here primarily focus on building and enhancing the RoboTurk system, or offering large-scale human datasets to the community.
CoRL 2018
ROBOTURK: A Crowdsourcing Platform for Robotic Skill Learning through Imitation
Initial RoboTurk system with a focus on simulation
[Arxiv]
[Website]
IROS 2019
Scaling Robot Supervision to Hundreds of Hours with RoboTurk
RoboTurk on Real World Tasks
[Arxiv]
[Website]
ICRA 2021
Learning Multi-Arm Manipulation Through Collaborative Teleoperation
RoboTurk on Simulated Multi-Arm Environments
[Arxiv]
[Website]
Arxiv 2020
Human-in-the-Loop Imitation Learning using Remote Teleoperation
RoboTurk with Human-in-the-Loop Interventions
[Arxiv]
[Website]
Arxiv 2021
What Matters in Learning from Offline Human Demonstrations for Robot Manipulation
Large-scale evaluation of learning from human datasets
[Arxiv]
[Website]
Projects Using RoboTurk System
These projects use the RoboTurk system to collect datasets and then use these datasets for other purposes.
IRIS: Implicit Reinforcement without Interaction at Scale for Learning Control from Offline Robot Manipulation Data
Ajay Mandlekar, Fabio Ramos, Byron Boots, Silvio Savarese, Li Fei-Fei, Animesh Garg, Dieter Fox
ICRA 2020
Learning to Generalize Across Long-Horizon Tasks from Human Demonstrations
Ajay Mandlekar*, Danfei Xu*, Roberto Martín-Martín, Silvio Savarese, Li Fei-Fei
RSS 2020
Generalization Through Hand-Eye Coordination: An Action Space for Learning Spatially-Invariant Visuomotor Control
Chen Wang*, Rui Wang*, Ajay Mandlekar, Li Fei-Fei, Silvio Savarese, Danfei Xu
IROS 2021
Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration
Chen Wang, Claudia Pérez-D'Arpino, Danfei Xu, Li Fei-Fei, C. Karen Liu, Silvio Savarese
Arxiv 2021
Our Team
Ajay Mandlekar
amandlek[at]stanford[dot]edu
Albert Tung
atung3[at]stanford[dot]edu
Josiah Wong
jdwong[at]stanford[dot]edu
Roberto Martín-Martín
roberto[dot]martinmartin[at]stanford[dot]edu
Yuke Zhu
yukez[at]stanford[dot]edu
Animesh Garg
garg[at]cs.stanford[dot]edu
Fei-Fei Li
feifeili[at]cs.stanford[dot]edu
Silvio Savarese
silvio[at]cs.stanford[dot]edu
Alumni
Jonathan Booher
Max Spero
Anchit Gupta
Andrew Kondrich
Matthew Ricks
Julian Gao
John Emmons
Emre Orbay
Peter Dun
Tieler Callazo
Amelia (Qingyun) Bian
Alex (Kaiyi) Fu
Roboturk in the News
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