# Learning Sensory-Motor Associations from Demonstration

## TL;DR

This paper introduces a method for learning reactive robot behaviors from human demonstrations using a reactive programming approach, enabling adaptable and robust sensor-motor associations represented in human-readable scripts.

## Contribution

It presents a novel approach combining reactive programming with sensor-motor association learning from minimal demonstrations, enhancing robot adaptability.

## Key findings

- Behaviors learned from a single demonstration.
- Sensor and motor primitives improve robustness.
- Reactive programming enables human-readable behavior scripts.

## Abstract

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.