Supplementary Material - Learning efficient haptic shape exploration with a rigid tactile sensor array
Fleer S, Moringen A, Klatzky RL, Ritter H (2019)
Bielefeld University.
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objects.hdf5
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**Recorded data set**
The file "objects.hdf5" contains the recorded data that was used in the experiments of this work. It is composed of 201 x 201 data points for each of the four different objects, leading to ~161000 data points in total. The code for generating the data set was written in ``python 2.7`` while the file itself was created using the [h5py](https://www.h5py.org/) package **File structure**
The file is built out of four containers with shapes (201, 201, 256): ``` objects.hdf5/ ├── object_1/ | └── (201, 201, 256) ├── object_2/ | └── (201, 201, 256) ├── object_3/ | └── (201, 201, 256) ├── object_4/ | └── (201, 201, 256) ``` The data is provided as triples that are containing the information of the location *x* and orientation *phi* of the utilized tactile sensor, together with its corresponding flattened and normalized pressure vector *p*, i.e. (*x* ,*phi* ,*p*).
The file "objects.hdf5" contains the recorded data that was used in the experiments of this work. It is composed of 201 x 201 data points for each of the four different objects, leading to ~161000 data points in total. The code for generating the data set was written in ``python 2.7`` while the file itself was created using the [h5py](https://www.h5py.org/) package **File structure**
The file is built out of four containers with shapes (201, 201, 256): ``` objects.hdf5/ ├── object_1/ | └── (201, 201, 256) ├── object_2/ | └── (201, 201, 256) ├── object_3/ | └── (201, 201, 256) ├── object_4/ | └── (201, 201, 256) ``` The data is provided as triples that are containing the information of the location *x* and orientation *phi* of the utilized tactile sensor, together with its corresponding flattened and normalized pressure vector *p*, i.e. (*x* ,*phi* ,*p*).
Stichworte
haptic exploration;
robotics simulation;
machine learning;
reinforcement learning;
attention control;
haptic glances;
exploratory procedures;
neural networks;
recurrent neural networks;
deep learning;
object classification
Erscheinungsjahr
2019
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https://pub.uni-bielefeld.de/record/2936475
Zitieren
Fleer S, Moringen A, Klatzky RL, Ritter H. Supplementary Material - Learning efficient haptic shape exploration with a rigid tactile sensor array. Bielefeld University; 2019.
Fleer, S., Moringen, A., Klatzky, R. L., & Ritter, H. (2019). Supplementary Material - Learning efficient haptic shape exploration with a rigid tactile sensor array. Bielefeld University. doi:10.4119/unibi/2936475
Fleer, Sascha, Moringen, Alexandra, Klatzky, Roberta L., and Ritter, Helge. 2019. Supplementary Material - Learning efficient haptic shape exploration with a rigid tactile sensor array. Bielefeld University.
Fleer, S., Moringen, A., Klatzky, R. L., and Ritter, H. (2019). Supplementary Material - Learning efficient haptic shape exploration with a rigid tactile sensor array. Bielefeld University.
Fleer, S., et al., 2019. Supplementary Material - Learning efficient haptic shape exploration with a rigid tactile sensor array, Bielefeld University.
S. Fleer, et al., Supplementary Material - Learning efficient haptic shape exploration with a rigid tactile sensor array, Bielefeld University, 2019.
Fleer, S., Moringen, A., Klatzky, R.L., Ritter, H.: Supplementary Material - Learning efficient haptic shape exploration with a rigid tactile sensor array. Bielefeld University (2019).
Fleer, Sascha, Moringen, Alexandra, Klatzky, Roberta L., and Ritter, Helge. Supplementary Material - Learning efficient haptic shape exploration with a rigid tactile sensor array. Bielefeld University, 2019.
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objects.hdf5
413.71 MB
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2019-07-15T08:52:26Z
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In sonstiger Relation
Learning efficient haptic shape exploration with a rigid tactile sensor array
Fleer S, Moringen A, Klatzky RL, Ritter H (2020)
PLOS ONE 15(1): e0226880.
Fleer S, Moringen A, Klatzky RL, Ritter H (2020)
PLOS ONE 15(1): e0226880.
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