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The Applied AI Lab (A2I) explores core challenges in AI and Machine Learning to enable robots to robustly and effectively operate in complex, real-world environments. Our research is guided by our vision to create machines which constantly improve through use in their dedicated workspace. In doing so we explore a number of intellectual challenges at the heart of robot learning such as machine introspection in perception and decision making, data efficient learning from demonstration, task-based and transfer learning and the learning of complex tasks via a curriculum of less complex ones. All the while our intellectual curiosity remains grounded in real-world robotics domains such as autonomous driving, logistics, manipulation or space exploration.

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Highlights

2206, 2017

Adversarial Domain Adaptation

Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation Abstract – Appearance changes due to weather and seasonal conditions represent a strong impediment to the robust implementation of machine learning systems in outdoor robotics. While [...]

704, 2017

Deep Tracking

Deep tracking in the wild: End-to-End Tracking and Semantic Segmentation Using Recurrent Neural Networks Abstract – This paper presents a novel approach for tracking static and dynamic objects for an autonomous vehicle operating in complex urban [...]