Clarifying the Half Full or Half Empty Question: Multimodal Container Classification

Artificial Neural Networks and Machine Learning – ICANN 2023 pages 444–456, doi: 10.1007/978-3-031-44207-0_37 - Sep 2023 Open Access
Associated documents :  
Multimodal integration is a key component of allowing robots to perceive the world. Multimodality comes with multiple challenges that have to be considered, such as how to integrate and fuse the data. In this paper, we compare different possibilities of fusing visual, tactile and proprioceptive data. The data is directly recorded on the NICOL robot in an experimental setup in which the robot has to classify containers and their content. Due to the different nature of the containers, the use of the modalities can wildly differ between the classes. We demonstrate the superiority of multimodal solutions in this use case and evaluate three fusion strategies that integrate the data at different time steps. We find that the accuracy of the best fusion strategy is 15% higher than the best strategy using only one singular sense.

 

@InProceedings{SKW23, 
 	 author =  {Spisak, Josua and Kerzel, Matthias and Wermter, Stefan},  
 	 title = {Clarifying the Half Full or Half Empty Question: Multimodal Container Classification}, 
 	 booktitle = {Artificial Neural Networks and Machine Learning – ICANN 2023},
 	 journal = {},
 	 number = {},
 	 volume = {},
 	 pages = {444–456},
 	 year = {2023},
 	 month = {Sep},
 	 publisher = {},
 	 doi = {10.1007/978-3-031-44207-0_37}, 
 }