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Multi-task learning ideally allows robots to acquire a diverse repertoire ofuseful skills. However, many multi-task reinforcement learning efforts assumethe robot can collect data from all tasks at all times. In reality, the tasksthat the robot learns arrive sequentially, depending on the user and therobot's current environment. In this work, we study a practical sequentialmulti-task RL problem that is motivated by the practical constraints ofphysical robotic systems, and derive an approach that effectively leverages thedata and policies learned for previous tasks to cumulatively grow the robot'sskill-set. In a series of simulated robotic manipulation experiments, ourapproach requires less than half the samples than learning each task fromscratch, while avoiding impractical round-robin data collection. On a FrankaEmika Panda robot arm, our approach incrementally learns ten challenging tasks,including bottle capping and block insertion.

Lifelong Robotic Reinforcement Learning by Retaining Experiences