Making deep learning models robust for object recognition

Robots have a wide range of applications from assisting humans around the factory, home, work office, in the field and more. But if we are to rely on them for assistive tasks, their perception algorithms need to be robust. This is what Professor Wolfram Burgard, head of research lab for Autonomous Intelligent Systems at the University of Freiburg, discussed at the 28th Australasian Joint Conference on Artificial Intelligence in Canberra this week. Burgard spoke about object recognition, as this is one of the fundamental capabilities of robots in many applications. “If we want to build robots that can actually act in the real world, then we need to have robust object detection,” he pointed out. “Robots need to be able to learn what we mean when we say ‘tomato juice’.…

Link to Full Article: Making deep learning models robust for object recognition

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