Approaches to probabilistic model learning for mobile manipulation robots

摘要

The current robotics community has a strong interest in the use of hybrid systems, such as mobile platforms with one or more robotic manipulators that can move autonomously and grasp tools or manipulate objects. The fusion of the advantages of mobile robots with manipulators leads to many applications, including domestic tasks, transportation, and inspection, among others. To achieve successful results in such dynamic and uncertain environments, it makes sense to adopt probabilistic learning techniques to control both the mobile platform and the manipulator. This book is framed within this context. The proposed strategies have been validated in physical experiments using real robots and in simulation. The only negative aspect of this book is that the title could be misleading, since no autonomous motion of a mobile platform is considered. This book only deals with robotic manipulators.