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We present a framework for data-driven robotics that makes use of a largedataset of recorded robot experience and scales to several tasks using learnedreward functions. We show how to apply this framework to accomplish threedifferent object manipulation tasks on a real robot platform. Givendemonstrations of a task together with task-agnostic recorded experience, weuse a special form of human annotation as supervision to learn a rewardfunction, which enables us to deal with real-world tasks where the rewardsignal cannot be acquired directly. Learned rewards are used in combinationwith a large dataset of experience from different tasks to learn a robot policyoffline using batch RL. We show that using our approach it is possible to trainagents to perform a variety of challenging manipulation tasks includingstacking rigid objects and handling cloth.

Scaling data-driven robotics with reward sketching and batch reinforcement learning