Context aware shared autonomy for robotic manipulation tasks

• Published in: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 5686 - 5693
• Main Authors: Witzig, Thomas, Zollner, J. Marius, Pangercic, Dejan, Osentoski, Sarah, Jakel, Rainer, Dillmann, Rudiger
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.11.2013
• Subjects: Bayes methods; Bayesian Network; Context; Context Awareness; Probabilistic logic; Probability distribution; Robotic Manipulation; Robots; Semantics; Shared Autonomy; User interfaces
• ISSN: 2153-0858
• DOI: 10.1109/IROS.2013.6697180

This paper describes a collaborative human-robot system that provides context information to enable more effective robotic manipulation. We take advantage of the semantic knowledge of a human co-worker who provides additional context information and interacts with the robot through a user interface. A Bayesian Network encodes the dependencies between this information provided by the user. The output of this model generates a ranked list of grasp poses best suitable for a given task which is then passed to the motion planner. Our system was implemented in ROS and tested on a PR2 robot. We compared the system to state-of-the-art implementations using quantitative (e.g. success rate, execution times) as well as qualitative (e.g. user convenience, cognitive load) metrics. We conducted a user study in which eight subjects were asked to perform a generic manipulation task, for instance to pour a bottle or move a cereal box, with a set of state-of-the-art shared autonomy interfaces. Our results indicate that an interface which is aware of the context provides benefits not currently provided by other state-of-the-art implementations.