1 research outputs found
Are we done with object recognition? The iCub robot's perspective
We report on an extensive study of the benefits and limitations of current
deep learning approaches to object recognition in robot vision scenarios,
introducing a novel dataset used for our investigation. To avoid the biases in
currently available datasets, we consider a natural human-robot interaction
setting to design a data-acquisition protocol for visual object recognition on
the iCub humanoid robot. Analyzing the performance of off-the-shelf models
trained off-line on large-scale image retrieval datasets, we show the necessity
for knowledge transfer. We evaluate different ways in which this last step can
be done, and identify the major bottlenecks affecting robotic scenarios. By
studying both object categorization and identification problems, we highlight
key differences between object recognition in robotics applications and in
image retrieval tasks, for which the considered deep learning approaches have
been originally designed. In a nutshell, our results confirm the remarkable
improvements yield by deep learning in this setting, while pointing to specific
open challenges that need be addressed for seamless deployment in robotics.Comment: 21 pages + supplementary materia