Peer-Assisted Robotic Learning (PARL) is an approach that leverages cloud robotic systems to enable data collaboration among local robots. By sharing data and models, robots can improve their learning capabilities and performance in tasks such as self-driving and localization. This collaborative approach allows robots to benefit from the experiences of other robots, leading to more accurate and efficient localization.