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

Understand

We present a framework for data-driven robotics that makes use of a large dataset of recorded robot experience and scales to several tasks using learned reward functions.

• We show how to apply this framework to accomplish three different object manipulation tasks on a real robot platform.
• Given demonstrations of a task together with task-agnostic recorded experience, we use a special form of human annotation as supervision to learn a reward function, which enables us to deal with real-world tasks where the reward signal cannot be acquired directly.
• Learned rewards are used in combination with a large dataset of experience from different tasks to learn a robot policy offline using batch RL.