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Reinforcement learning (RL) algorithms hold the promise of enablingautonomous skill acquisition for robotic systems. However, in practice,real-world robotic RL typically requires time consuming data collection andfrequent human intervention to reset the environment. Moreover, roboticpolicies learned with RL often fail when deployed beyond the carefullycontrolled setting in which they were learned. In this work, we study how thesechallenges can all be tackled by effective utilization of diverse offlinedatasets collected from previously seen tasks. When faced with a new task, oursystem adapts previously learned skills to quickly learn to both perform thenew task and return the environment to an initial state, effectively performingits own environment reset. Our empirical results demonstrate that incorporatingprior data into robotic reinforcement learning enables autonomous learning,substantially improves sample-efficiency of learning, and enables bettergeneralization. Project website: https://sites.google.com/view/ariel-berkeley/

Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning