29th IEEE International Conference on Robot and Human Interactive Communication (Ro-Man 2020), August 2020 (conference) Accepted
We propose a method which generates reactive
robot behavior learned from human demonstration. In order
to do so, we use the Playful programming language which is
based on the reactive programming paradigm. This allows us to
represent the learned behavior as a set of associations between
sensor and motor primitives in a human readable script.
Distinguishing between sensor and motor primitives introduces
a supplementary level of granularity and more importantly
enforces feedback, increasing adaptability and robustness. As
the experimental section shows, useful behaviors may be learned
from a single demonstration covering a very limited portion of
the task space.
Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems