Details
| Original language | English |
|---|---|
| Title of host publication | 2024 International Joint Conference on Neural Networks |
| Subtitle of host publication | IJCNN 2024 - Proceedings |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 8 |
| ISBN (electronic) | 9798350359312 |
| ISBN (print) | 979-8-3503-5932-9 |
| Publication status | Published - 2024 |
| Event | 2024 International Joint Conference on Neural Networks, IJCNN 2024 - Yokohama, Japan Duration: 30 Jun 2024 → 5 Jul 2024 |
Abstract
Tactile sensing presents a promising opportunity for enhancing the interaction capabilities of today's robots. BioTac is a commonly used tactile sensor that enables robots to perceive and respond to physical tactile stimuli. However, the sensor's non-linearity poses challenges in simulating its behavior. In this paper, we first investigate a BioTac simulation that uses temperature, force, and contact point positions to predict the sensor outputs. We show that training with BioTac temperature readings does not yield accurate sensor output predictions during deployment. Consequently, we tested three alternative models, i.e., an XGBoost regressor, a neural network, and a transformer encoder. We train these models without temperature readings and provide a detailed investigation of the window size of the input vectors. We demonstrate that we achieve statistically significant improvements over the baseline network. Furthermore, our results reveal that the XGBoost regressor and transformer outperform traditional feed-forward neural networks in this task. We make all our code and results available online on https://github.com/wzaielamri/Optimizing-BioTac-Simulation.
Keywords
- BioTac, Tactile Perception, Transformer, XGBoost
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
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2024 International Joint Conference on Neural Networks: IJCNN 2024 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2024.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Optimizing BioTac Simulation for Realistic Tactile Perception
AU - Zai El Amri, Wadhah
AU - Navarro-Guerrero, Nicolás
N1 - Publisher Copyright: © 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Tactile sensing presents a promising opportunity for enhancing the interaction capabilities of today's robots. BioTac is a commonly used tactile sensor that enables robots to perceive and respond to physical tactile stimuli. However, the sensor's non-linearity poses challenges in simulating its behavior. In this paper, we first investigate a BioTac simulation that uses temperature, force, and contact point positions to predict the sensor outputs. We show that training with BioTac temperature readings does not yield accurate sensor output predictions during deployment. Consequently, we tested three alternative models, i.e., an XGBoost regressor, a neural network, and a transformer encoder. We train these models without temperature readings and provide a detailed investigation of the window size of the input vectors. We demonstrate that we achieve statistically significant improvements over the baseline network. Furthermore, our results reveal that the XGBoost regressor and transformer outperform traditional feed-forward neural networks in this task. We make all our code and results available online on https://github.com/wzaielamri/Optimizing-BioTac-Simulation.
AB - Tactile sensing presents a promising opportunity for enhancing the interaction capabilities of today's robots. BioTac is a commonly used tactile sensor that enables robots to perceive and respond to physical tactile stimuli. However, the sensor's non-linearity poses challenges in simulating its behavior. In this paper, we first investigate a BioTac simulation that uses temperature, force, and contact point positions to predict the sensor outputs. We show that training with BioTac temperature readings does not yield accurate sensor output predictions during deployment. Consequently, we tested three alternative models, i.e., an XGBoost regressor, a neural network, and a transformer encoder. We train these models without temperature readings and provide a detailed investigation of the window size of the input vectors. We demonstrate that we achieve statistically significant improvements over the baseline network. Furthermore, our results reveal that the XGBoost regressor and transformer outperform traditional feed-forward neural networks in this task. We make all our code and results available online on https://github.com/wzaielamri/Optimizing-BioTac-Simulation.
KW - BioTac
KW - Tactile Perception
KW - Transformer
KW - XGBoost
UR - http://www.scopus.com/inward/record.url?scp=85204952619&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2404.10425
DO - 10.48550/arXiv.2404.10425
M3 - Conference contribution
AN - SCOPUS:85204952619
SN - 979-8-3503-5932-9
BT - 2024 International Joint Conference on Neural Networks
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2024 International Joint Conference on Neural Networks, IJCNN 2024
Y2 - 30 June 2024 through 5 July 2024
ER -