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

arXiv: https://arxiv.org/abs/2010.08303 · HTML: https://mlsanity.com/paper/2010.08303/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2020.

## Built on by (most cited first, 9 of 9)

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- [A Secure and Efficient Multi-Object Grasping Detection Approach for Robotic Arms](https://mlsanity.com/paper/2209.03511/) (2022)
- [iCOIL: Scenario Aware Autonomous Parking Via Integrated Constrained Optimization and Imitation Learning](https://mlsanity.com/paper/2305.13663/) (2023)
- [Improving Efficiency of DNN-based Relocalization Module for Autonomous Driving with Server-side Computing](https://mlsanity.com/paper/2312.00316/) (2023)
- [Scene 3-D Reconstruction System in Scattering Medium](https://mlsanity.com/paper/2312.09005/) (2023)
- [MV-Adapter: Enhancing Underwater Instance Segmentation via Adaptive Channel Attention](https://mlsanity.com/paper/2411.00472/) (2024)

## Abstract (arXiv)

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.