Titlebook: Inductive Biases in Machine Learning for Robotics and Control; Michael Lutter Book 2023 The Editor(s) (if applicable) and The Author(s), u

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Introduction, by carefully engineering separate modules for each part. These modules are connected through an interpretable interface. For example, one module perceives the environment and estimates the engineered system state. The next module uses the state together with a simulator to plan future actions. The

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Combining Physics and Deep Learning for Continuous-Time Dynamics Models,ing approaches involve a deep network as part of their algorithm. The network either represents a policy that selects the actions, a dynamics model that predicts the next state, or a state estimator that extracts the relevant features from unstructured observations. Initially, many of these approach

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Michael Lutterthe Internet community, many solutions have been proposed to supply QoS to this protocol. Traditionally, DiffServ architectures have considered two-colour markings in order to distinguish between high and low priorities. We investigate the special treatment for web traffic, whose pattern is very clo

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Michael Luttereakthroughs. However, the field has not matured into a key engineering discipline which is heavily called upon by computer system and software engineers, even though it is well-known that already the use of simple analytical models can result in better insight in system performance. In the area of c

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Michael Lutterion between the areas of real-time systems and performance engineering. We address probabilistic schedulability analysis of tasks in single-processor non-preemptive real-time systems. To?this end, we consider periodic tasks with offsets, scheduled by Fixed Priority?(FP) or Earliest Deadline First?(E