A Humanoid Robot Learning Audiovisual Classification By Active Exploration

<p>We present a novel neurorobotic setup and dataset for active object exploration and audiovisual classification based on their material properties. In the robotic setup, a humanoid drops an item on a sloped surface and records the video image frames and raw audio of the collision of the surface and object. The novel dataset includes 32800 images and 1600 s of audio recording from 800 samples for 16 objects and will be made publicly available. We propose a novel neural architecture for the classification of the objects. A detailed analysis of results shows that different materials are easier classified either in the audio or the visual modality. As a main contribution, we can show that combining modalities can achieve an even higher classification accuracy of 90%.</p>

@InProceedings{MKSW21, 
 	 author =  {Mir, Glareh and Kerzel, Matthias and Strahl, Erik and Wermter, Stefan},  
 	 title = {A Humanoid Robot Learning Audiovisual Classification By Active Exploration}, 
 	 booktitle = {2021 IEEE International Conference on Development and Learning (ICDL)},
 	 editors = {},
 	 number = {},
 	 volume = {},
 	 pages = {1--6},
 	 year = {2021},
 	 month = {Aug},
 	 publisher = {IEEE},
 	 doi = {10.1109/ICDL49984.2021.9515598}, 
 }