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A technological revolution is occurring in the field of robotics with thedata-driven deep learning technology. However, building datasets for each localrobot is laborious. Meanwhile, data islands between local robots make dataunable to be utilized collaboratively. To address this issue, the work presentsPeer-Assisted Robotic Learning (PARL) in robotics, which is inspired by thepeer-assisted learning in cognitive psychology and pedagogy. PARL implementsdata collaboration with the framework of cloud robotic systems. Both data andmodels are shared by robots to the cloud after semantic computing and traininglocally. The cloud converges the data and performs augmentation, integration,and transferring. Finally, fine tune this larger shared dataset in the cloud tolocal robots. Furthermore, we propose the DAT Network (Data Augmentation andTransferring Network) to implement the data processing in PARL. DAT Network canrealize the augmentation of data from multi-local robots. We conductexperiments on a simplified self-driving task for robots (cars). DAT Networkhas a significant improvement in the augmentation in self-driving scenarios.Along with this, the self-driving experimental results also demonstrate thatPARL is capable of improving learning effects with data collaboration of localrobots.

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