A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning
Cyrus Neary,Christian Ellis,Aryaman Singh Samyal,Craig Lennon,Ufuk Topcu,Cyrus Neary,Christian Ellis,Aryaman Singh Samyal,Craig Lennon,Ufuk Topcu
We propose and demonstrate a compositional framework for training and verifying reinforcement learning (RL) systems within a multifidelity sim-to-real pipeline, in order to deploy reliable and adaptable RL policies on physical hardware. By decomposing complex robotic tasks into component subtasks and defining mathematical interfaces between them, the framework allows for the independent training a...
