Peer-Assisted Robotic Learning: A Data-Driven Collaborative Learning Approach for Cloud Robotic Systems

TL;DR

This paper introduces Peer-Assisted Robotic Learning (PARL), a collaborative framework for cloud robotic systems that enhances data sharing and model training among robots using a novel data augmentation network, improving learning efficiency.

Contribution

The work presents a new data-driven collaborative learning framework for robots, including a novel DAT Network for data augmentation and transfer, enabling effective data sharing and improved learning in cloud robotic systems.

Findings

DAT Network significantly improves data augmentation in self-driving scenarios.

PARL enhances learning outcomes through collaborative data sharing among robots.

Experimental results demonstrate improved performance in a robot self-driving task.

Abstract

A technological revolution is occurring in the field of robotics with the data-driven deep learning technology. However, building datasets for each local robot is laborious. Meanwhile, data islands between local robots make data unable to be utilized collaboratively. To address this issue, the work presents Peer-Assisted Robotic Learning (PARL) in robotics, which is inspired by the peer-assisted learning in cognitive psychology and pedagogy. PARL implements data collaboration with the framework of cloud robotic systems. Both data and models are shared by robots to the cloud after semantic computing and training locally. The cloud converges the data and performs augmentation, integration, and transferring. Finally, fine tune this larger shared dataset in the cloud to local robots. Furthermore, we propose the DAT Network (Data Augmentation and Transferring Network) to implement the data processing in PARL. DAT Network can realize the augmentation of data from multi-local robots. We conduct experiments on a simplified self-driving task for robots (cars). DAT Network has a significant improvement in the augmentation in self-driving scenarios. Along with this, the self-driving experimental results also demonstrate that PARL is capable of improving learning effects with data collaboration of local robots.