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

@inproceedings{Matas:2018,
author = {Matas, J and James, S and Davison, A},
pages = {734--743},
publisher = {PMLR},
title = {Sim-to-real reinforcement learning for deformable object manipulation},
url = {http://hdl.handle.net/10044/1/64366},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We have seen much recent progress in rigid object manipulation, but in-teraction with deformable objects has notably lagged behind. Due to the large con-figuration space of deformable objects, solutions using traditional modelling ap-proaches require significant engineering work.  Perhaps then, bypassing the needfor explicit modelling and instead learning the control in an end-to-end mannerserves as a better approach? Despite the growing interest in the use of end-to-endrobot learning approaches, only a small amount of work has focused on their ap-plicability to deformable object manipulation. Moreover, due to the large amountof data needed to learn these end-to-end solutions, an emerging trend is to learncontrol policies in simulation and then transfer them over to the real world.  To-date, no work has explored whether it is possible to learn and transfer deformableobject policies.  We believe that if sim-to-real methods are to be employed fur-ther, then it should be possible to learn to interact with a wide variety of objects,and not only rigid objects.  In this work, we use a combination of state-of-the-artdeep reinforcement learning algorithms to solve the problem of manipulating de-formable objects (specifically cloth). We evaluate our approach on three tasks —folding a towel up to a mark, folding a face towel diagonally, and draping a pieceof cloth over a hanger.  Our agents are fully trained in simulation with domainrandomisation, and then successfully deployed in the real world without havingseen any real deformable objects.
AU  - Matas,J
AU  - James,S
AU  - Davison,A
EP  - 743
PB  - PMLR
PY  - 2018///
SP  - 734
TI  - Sim-to-real reinforcement learning for deformable object manipulation
UR  - http://hdl.handle.net/10044/1/64366
ER  -
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