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Imitation learning has allowed recent advances in learning robotic manipulation tasks but has been limited due to the scarcity of high quality demonstrations to learn from.
RoboTurk is a system that help solve this problem by enabling the rapid crowdsourcing of high-quality demonstrations. This allows the creation of large datasets for manipulation tasks that we show improves the quality of imitation learning policies.
We have recently extended the RoboTurk platform to work with real robots and presented this work at IROS 2024. Click the link above to learn more.
RoboTurk is a research project and dataset from the Stanford Vision Lab. The research paper and sample data are available publicly.
Use RoboTurk from anywhere. Even the top of a mountain.
RoboTurk in action.
RoboTurk is designed to be modular and enables quick imitation guided skill learning. All you need to do is:
Behind the scenes, every time a user connects to the RoboTurk platform
The "picking" task
The "assembly" task
RoboTurk in the real world
We designed and used two simulated environments made using MuJoCo: a "picking" task where users are asked to sort objects on one side into corresponding bins on the other, an "assembly" task where users are asked to put a "nut" on the correct peg. However, RoboTurk is not constrained to a specific simulator, environment, or even a simulator at all. All you need in order to gather data in the real world is a few robots.
Using RoboTurk, we were able to collect a dataset of demonstrations on the two tasks that we created. We used the same annotators over the course of the 4 day span it took to gather this dataset and the rate at which they produced successful demonstrations increased over time as they became more adept at using the system.
We tested RoboTurk using 4 different user interfaces (UIs). With allow different types of movement.
Based on task completion times, the VR and phone interfaces are comparable while the phone is significantly more widely accessible.
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We found that is possible to collect quality demonstrations using RoboTurk regardless of the distance between user and server.
The results we present use the full dataset of demonstrations that we gathered using RoboTurk. We learn policies using a combination of reinforcement learning and imitation learning guided by the data gathered by RoboTurk. Specifically, policies were learned using a distributed version of PPO (DPPO) from Surreal where the initial states for each episode are taken from along trajectories in the dataset. This helps overcome the exploration problem for difficult tasks.
An example learned policy on a simplified version of the "picking" task that we call "can picking".
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This table represents policy performance on the task after 24 hours for the can picking task and 48 hours on the round assembly task. Note that the maximum possible return is 1000 which corresponds to instantaneous solving of the task. It is clear that using 1000 demonstrations for each task (nearly all of the RoboTurk dataset) performs the best on both tasks.
Ph.D. Student, EE
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Ph.D. Student, CS
yukez[at]stanford[dot]edu
Postdoctoral Researcher
roberto[dot]martinmartin[at]stanford[dot]edu
Postdoctoral Researcher
garg[at]cs.stanford[dot]edu
Undergraduate Student, CS
jaustinb[at]stanford[dot]edu
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atung3[at]stanford[dot]edu
Undergraduate Student, CS
andrewk1[at]stanford[dot]edu
Masters Student, ME
ricks[at]stanford[dot]edu
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silvio[at]cs.stanford[dot]edu
Professor, CS
feifeili[at]cs.stanford[dot]edu
Master's Student, CS
maxspero[at]stanford[dot]edu
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atung3[at]stanford[dot]edu
Ph.D. Student, CS
jemmons[at]stanford[dot]edu
Master's Student, CS
anchitg[at]stanford[dot]edu
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eorbay[at]stanford[dot]edu
Undergraduate Student, CS
bodun[at]stanford[dot]edu
Master's Student, ME
tieler[at]stanford[dot]edu
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qb1[at]stanford[dot]edu
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alexfu6[at]stanford[dot]edu
All a user needs to do is:
The video shows just how easy it is to interact with RoboTurk to generate a demonstration.
The data is available here
@inproceedings{mandlekar2018roboturk,
title={RoboTurk: A Crowdsourcing Platform for Robotic Skill Learning through Imitation},
author={Mandlekar, Ajay and Zhu, Yuke and Garg, Animesh and Booher, Jonathan and Spero, Max and Tung, Albert and Gao, Julian and Emmons, John and Gupta, Anchit and Orbay, Emre and Savarese, Silvio and Fei-Fei, Li},
booktitle={Conference on Robot Learning}, year={2018} }
