Learning kinematic models for articulated objects
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Imitation learning with generalized task descriptions
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Object identification with tactile sensors using bag-of-features
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
A probabilistic framework for learning kinematic models of articulated objects
Journal of Artificial Intelligence Research
Tactile Sensing for Mobile Manipulation
IEEE Transactions on Robotics
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
Approaches to Probabilistic Model Learning for Mobile Manipulation Robots
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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.