Learning Probabilistic Models for Mobile Manipulation Robots

Mobile manipulation robots are envisioned to provide many useful services both in domestic environments as well as in the industrial context.  In this paper, we present novel approaches to allow mobile maniplation systems to autonomously adapt to new or changing situations. The approaches developed in this paper cover the following four topics: (1) learning the robot's kinematic structure and properties using actuation and visual feedback, (2) learning about articulated objects in the environment in which the robot is operating, (3) using tactile feedback to augment visual perception, and (4) learning novel manipulation tasks from human demonstrations.