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I am interested in interactive perception and task-directed exploration, two related and deeply robotic problems.
Interactive perception is important, as not all information that is relevant to an agent is readily available just from looking at the world. The agent needs to exert forces, interact with the world to reveal what's relevant, e.g. the weight of an object, or the degrees of freedom of a kinematic structure. Furthermore, as the agent knows which actions it performed to generate sensor data, it can make use of that information to interpret its input.
The world is complex, but robots are usually employed to solve a set of tasks for which they just need to know about a subset of the world. This is why it is important not to explore the environment randomly, but to perform task-directed exploration. But how to find out which information is actually relevant to a task? And how can we gather that information? I aim to answer these questions in my research.
|Author||Manuel Baum and Martin Meier and Malte Schilling|
|Title of Book||2015 International Joint Conference on Neural Networks (IJCNN)|
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