Below is a list of all relevant publications authored by Robotics Forum members.

Citation

BibTex format

@inproceedings{Ding:2020:10.1109/ICRA40945.2020.9197512,
author = {Ding, Z and Lepora, N and Johns, E},
doi = {10.1109/ICRA40945.2020.9197512},
pages = {1639--1645},
publisher = {IEEE},
title = {Sim-to-real transfer for optical tactile sensing},
url = {http://dx.doi.org/10.1109/ICRA40945.2020.9197512},
year = {2020}
}

RIS format (EndNote, RefMan)

TY  - CPAPER
AB - Deep learning and reinforcement learning meth-ods have been shown to enable learning of flexible and complexrobot controllers. However, the reliance on large amounts oftraining data often requires data collection to be carried outin simulation, with a number of sim-to-real transfer methodsbeing developed in recent years. In this paper, we study thesetechniques for tactile sensing using the TacTip optical tactilesensor, which consists of a deformable tip with a cameraobserving the positions of pins inside this tip. We designeda model for soft body simulation which was implemented usingthe Unity physics engine, and trained a neural network topredict the locations and angles of edges when in contact withthe sensor. Using domain randomisation techniques for sim-to-real transfer, we show how this framework can be used toaccurately predict edges with less than 1 mm prediction errorin real-world testing, without any real-world data at all.
AU - Ding,Z
AU - Lepora,N
AU - Johns,E
DO - 10.1109/ICRA40945.2020.9197512
EP - 1645
PB - IEEE
PY - 2020///
SN - 2152-4092
SP - 1639
TI - Sim-to-real transfer for optical tactile sensing
UR - http://dx.doi.org/10.1109/ICRA40945.2020.9197512
UR - https://ieeexplore.ieee.org/abstract/document/9197512
UR - http://hdl.handle.net/10044/1/78459
ER -

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