Imperial College London
Alternate

ProfessorAndrewDavison

Faculty of Engineering, Department of Computing

Professor of Robot Vision
 
 
 
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Contact

 

+44 (0)20 7594 8316a.davison Website

 
 
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Assistant

 

Ms Lucy Atthis +44 (0)20 7594 8259

 
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Location

 

303William Penney LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bonardi:2020:10.1109/lra.2020.2977835,
author = {Bonardi, A and James, S and Davison, AJ},
doi = {10.1109/lra.2020.2977835},
journal = {IEEE Robotics and Automation Letters},
pages = {3533--3539},
title = {Learning one-shot imitation from humans without humans},
url = {http://dx.doi.org/10.1109/lra.2020.2977835},
volume = {5},
year = {2020}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Humans can naturally learn to execute a new task by seeing it performed by other individuals once, and then reproduce it in a variety of configurations. Endowing robots with this ability of imitating humans from third person is a very immediate and natural way of teaching new tasks. Only recently, through meta-learning, there have been successful attempts to one-shot imitation learning from humans; however, these approaches require a lot of human resources to collect the data in the real world to train the robot. But is there a way to remove the need for real world human demonstrations during training? We show that with Task-Embedded Control Networks, we can infer control polices by embedding human demonstrations that can condition a control policy and achieve one-shot imitation learning. Importantly, we do not use a real human arm to supply demonstrations during training, but instead leverage domain randomisation in an application that has not been seen before: sim-to-real transfer on humans. Upon evaluating our approach on pushing and placing tasks in both simulation and in the real world, we show that in comparison to a system that was trained on real-world data we are able to achieve similar results by utilising only simulation data. Videos can be found here: https://sites.google.com/view/tecnets-humans .
AU  - Bonardi,A
AU  - James,S
AU  - Davison,AJ
DO  - 10.1109/lra.2020.2977835
EP  - 3539
PY  - 2020///
SN  - 2377-3766
SP  - 3533
TI  - Learning one-shot imitation from humans without humans
T2  - IEEE Robotics and Automation Letters
UR  - http://dx.doi.org/10.1109/lra.2020.2977835
UR  - https://ieeexplore.ieee.org/document/9020095
UR  - http://hdl.handle.net/10044/1/78645
VL  - 5
ER  -
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