Abstract
Robots are usually equipped with many different sensors that need to be integrated. While most research is focused on the integration of vision with other senses, we successfully integrate tactile and auditory sensor data from a complex robotic system. Herein, we train and evaluate a neural network for the classification of the content of eight optically identical medicine containers. To investigate the relevance of the tactile modality in classification under realistic conditions, we apply different noise levels to the audio data. Our results show significantly higher robustness to acoustic noise with the combined multimodal network than with the unimodal audio based counterpart.
This research was funded by the German Research Foundation (DFG) and the National Science Foundation of China in project Crossmodal Learning, TRR-169. It is also partially financed by the H2020-MSCA-RISE Project ULTRACEPT. Manfred Eppe acknowledges support via the DFG-funded IDEAS (EP 143/2-1) and LeCAREbot (EP 143/4-1) projects.
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Notes
- 1.
To enable control over the acoustic noise, we used a high-quality external microphone and added separately recorded noise of the robot to the signal during the evaluation.
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Jonetzko, Y., Fiedler, N., Eppe, M., Zhang, J. (2021). Multimodal Object Analysis with Auditory and Tactile Sensing Using Recurrent Neural Networks. In: Sun, F., Liu, H., Fang, B. (eds) Cognitive Systems and Signal Processing. ICCSIP 2020. Communications in Computer and Information Science, vol 1397. Springer, Singapore. https://doi.org/10.1007/978-981-16-2336-3_23
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