Titlebook: Approaches to Probabilistic Model Learning for Mobile Manipulation Robots; Jürgen Sturm Book 2013 Springer-Verlag Berlin Heidelberg 2013 A

accrete

Learning Manipulation Tasks by Demonstration,tential tasks of a manipulation robot beforehand. For example, robotic assistants operating in industrial contexts are frequently faced with changes in the production process. As a consequence, novel manipulation skills become relevant on a regular basis. For this reason, there is a need for solutio

DAMN

朦朧

hangdog

https://doi.org/10.1007/978-3-658-02802-2nt in Section 2.2 several measures to evaluate the quality of a model and to select the best one. Finally, we introduce in Section 2.3 Bayesian networks as a tool to factorize high-dimensional learning problems into independent components.

minion

nutrition

QUAIL

乞丐

https://doi.org/10.1007/978-3-322-82883-5er. In particular for robotic manipulation tasks, tactile sensing provides another sensor modality that can reveal relevant aspects about the object being manipulated, for example, to infer its identity, pose, and internal state.

pineal-gland

Meyer-Hentschel Management Consultingn the production process. As a consequence, novel manipulation skills become relevant on a regular basis. For this reason, there is a need for solutions that enable normal users to quickly and intuitively teach new manipulation skills to a robot.

HATCH

Meyer-Hentschel Management Consultingquire that robots function robustly in new situations while they are dealing with considerable amounts of noise and uncertainty. Therefore, the main objective of this work was to develop novel approaches that enable manipulation robots to autonomously acquire the models they need to successfully implement their service tasks.