Robot Learning Language
The Robot Learning Language (RoLL) is an extension of the CRAM Plan Language to integrate continual learning functionality into robot control programs. For further details see the reference manual and publications listed below.
Code
The code is available as a ROS stack: http://wiki.ros.org/roll
Manual
Publications
Learning Ability Models for Human-Robot Collaboration (Alexandra Kirsch, Fan Cheng), In Robotics: Science and Systems (RSS) --- Workshop on Learning for Human-Robot Interaction Modeling, 2010. [pdf]
Robot Learning Language --- Integrating Programming and Learning for Cognitive Systems (Alexandra Kirsch), In Robotics and Autonomous Systems Journal, volume 57, 2009. [pdf]
An Integrated Planning and Learning Framework for Human-Robot Interaction (Alexandra Kirsch, Thibault Kruse, Lorenz Mösenlechner), In 4th Workshop on Planning and Plan Execution for Real-World Systems (held in conjuction with ICAPS 09), 2009. [pdf]
Integration of Programming and Learning in a Control Language for Autonomous Robots Performing Everyday Activities (Alexandra Kirsch), PhD thesis, Technische Universität München, 2008. [pdf]
Training on the Job --- Collecting Experience with Hierarchical Hybrid Automata (Alexandra Kirsch, Michael Beetz), In Proceedings of the 30th German Conference on Artificial Intelligence (KI-2007) (J. Hertzberg, M. Beetz, R. Englert, eds.), 2007. [pdf]
The Assistive Kitchen --- A Demonstration Scenario for Cognitive Technical Systems (Michael Beetz, Jan Bandouch, Alexandra Kirsch, Alexis Maldonado, Armin Müller, Radu Bogdan Rusu), In Proceedings of the 4th COE Workshop on Human Adaptive Mechatronics (HAM), 2007. [pdf]
Making Robot Learning Controllable: A Case Study in Robot Navigation (Alexandra Kirsch, Michael Schweitzer, Michael Beetz), In Proceedings of the ICAPS Workshop on Plan Execution: A Reality Check, 2005. [pdf]
RPL-LEARN: Extending an Autonomous Robot Control Language to Perform Experience-based Learning (Michael Beetz, Alexandra Kirsch, Armin Müller), In 3rd International Joint Conference on Autonomous Agents & Multi Agent Systems (AAMAS), 2004. [pdf]