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

Homer Rich Walke, Jonathan Heewon Yang, Albert Yu, Aviral Kumar, Jędrzej Orbik, Avi Singh, Sergey Levine

Keywords: autonomous RL, offline RL, reset-free manipulation

TL;DR: Incorporating prior data into robotic reinforcement learning enables autonomous learning, substantially improves sample-efficiency of learning, and enables better generalization.

Abstract:

Reinforcement learning (RL) algorithms hold the promise of enabling autonomous skill acquisition for robotic systems. However, in practice, real-world robotic RL typically requires time consuming data collection and frequent human intervention to reset the environment. Moreover, robotic policies learned with RL often fail when deployed beyond the carefully controlled setting in which they were learned.
In this work, we study how these challenges of real-world robotic learning can all be tackled by effective utilization of diverse offline datasets collected from previously seen tasks. When faced with a new task, our system adapts previously learned skills to quickly learn to both perform the new task and return the environment to an initial state, effectively performing its own environment reset.
Our empirical results demonstrate that incorporating prior data into robotic reinforcement learning enables autonomous learning, substantially improves sample-efficiency of learning, and enables better generalization.

Student First Author: yes

Website: https://sites.google.com/view/ariel-berkeley