# Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach

## TL;DR

This paper presents a deep learning method enabling heterogeneous robots to transfer cleaning task knowledge using visual feedback, demonstrating robustness to environmental variations and successful cross-robot application.

## Contribution

It introduces a convolutional neural network approach for transferring cleaning task knowledge between different robots and environments, enhancing robustness through data augmentation.

## Key findings

- Successful transfer of cleaning skills from iCub to DoRo robot
- Robustness achieved against posture and illumination changes
- Effective generalization to unseen dirt patterns

## Abstract

Nowadays, autonomous service robots are becoming an important topic in robotic research. Differently from typical industrial scenarios, with highly controlled environments, service robots must show an additional robustness to task perturbations and changes in the characteristics of their sensory feedback. In this paper, a robot is taught to perform two different cleaning tasks over a table, using a learning from demonstration paradigm. However, differently from other approaches, a convolutional neural network is used to generalize the demonstrations to different, not yet seen dirt or stain patterns on the same table using only visual feedback, and to perform cleaning movements accordingly. Robustness to robot posture and illumination changes is achieved using data augmentation techniques and camera images transformation. This robustness allows the transfer of knowledge regarding execution of cleaning tasks between heterogeneous robots operating in different environmental settings. To demonstrate the viability of the proposed approach, a network trained in Lisbon to perform cleaning tasks, using the iCub robot, is successfully employed by the DoRo robot in Peccioli, Italy.