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Throughout the past decade, neural network-based vision has been increasingly integrated into robots. It is well-known, however, that neural networks can be unreliable, especially when faced with inputs that differ from their training data. With the goal of making neural network-based vision more reliable for robotic applications, this thesis explores uncertainty estimation, and adaptability.A
