Title :
The Teaching-Box: A universal robot learning framework
Author :
Ertel, Wolfgang ; Schneider, Markus ; Cubek, Richard ; Tokic, Michel
Author_Institution :
Univ. of Appl. Sci. Ravensburg-Weingarten, Weingarten, Germany
Abstract :
There exist many powerful machine learning software libraries, which help the engineer to build robots that learn autonomously. However, engineering of an autonomous robot still is a challenging and time consuming task even with these learning libraries. With the open source Teaching-Box presented here, the “training” of a robot becomes easier due to the following features. The Java library of the Teaching-Box provides algorithms for reinforcement learning as well as for learning by demonstration (utilizing supervised learning algorithms) and data structures for exchanging policies between the different ways of learning. As an initial policy one can even take a manually coded behaviour and then improve it for example with reinforcement learning. A human trainer feedback (e.g. via the speech interface) can be used to increase the learning speed. The Eclipse based GUI facilitates the design of the robot learning projects and visualizes the learning process. For connecting the various modules of a project, open interface standards such as RL-Glue are used and an easy integration of the Teaching-Box into standard robot middleware is possible.
Keywords :
Java; data structures; intelligent robots; learning (artificial intelligence); teaching; Eclipse based GUI; Java library; autonomous robot; data structure; human trainer feedback; learning libraries; learning process; learning speed; open source teaching-box; powerful machine learning software libraries; reinforcement learning; supervised learning; teaching box; universal robot learning framework; Data structures; Educational robots; Feedback; Humans; Java; Machine learning; Power engineering and energy; Software libraries; Speech; Supervised learning;
Conference_Titel :
Advanced Robotics, 2009. ICAR 2009. International Conference on
Conference_Location :
Munich
Print_ISBN :
978-1-4244-4855-5