Robot Learning for Persistent Autonomy

Post date: May 29, 2016 3:46:57 PM

Petar kormushev, Seyed Reza Ahmadzadeh

Reference:

Petar Kormushev, Seyed Reza Ahmadzadeh, “Robot Learning for Persistent Autonomy”, Chapter in

Handling Uncertainty and Networked Structure in Robot Control (Lucian Busoniu, Levente Tamás, eds.),

Springer International Publishing, pp. 3-28, 2015.

Bibtex Entry:

@INBOOK{kormushev2015chapterrobot, AUTHOR={Kormushev, Petar and Ahmadzadeh, Seyed Reza}, EDITOR={Busoniu, Lucian and Tam{\'a}s, Levente}, TITLE={Robot Learning for Persistent Autonomy}, BOOKTITLE={Handling Uncertainty and Networked Structure in Robot Control}, YEAR={2015}, PUBLISHER={Springer International Publishing}, ADDRESS={Cham}, PAGES={3--28}, ISBN={978-3-319-26327-4}, DOI={10.1007/978-3-319-26327-4_1}, URL={http://dx.doi.org/10.1007/978-3-319-26327-4_1} }

DOI:

10.1007/978-3-319-26327-4_1

Abstract:

Autonomous robots are not very good at being autonomous. They work well in structured environments,

but fail quickly in the real world facing uncertainty and dynamically changing conditions. In this

chapter, we describe robot learning approaches that help to elevate robot autonomy to the next

level, the so-called 'persistent autonomy'. For a robot to be 'persistently autonomous' means to be

able to perform missions over extended time periods (e.g. days or months) in dynamic, uncertain

environments without need for human assistance. In particular, persistent autonomy is extremely

important for robots in difficult-to-reach environments such as underwater, rescue, and space

robotics. There are many facets of persistent autonomy, such as: coping with uncertainty, reacting

to changing conditions, disturbance rejection, fault tolerance, energy efficiency and so on. This

chapter presents a collection of robot learning approaches that address many of these facets.

Experiments with robot manipulators and autonomous underwater vehicles demonstrate the usefulness

of these learning approaches in real world scenarios.