Deploying Reinforcement Learning in the Real World: A Case Study on Apptronik Apollo

Abstract

Master of ScienceDeep reinforcement learning (RL) has gained increasing popularity as an approach to achieving dynamic behaviors on legged robots. However, transferring RL behaviors from simulation to reality is a challenging process: imperfect sensors, simulation models, control architecture, and latency all present obstacles to successfully deploying such approaches in the real world. In this thesis, we present an end-to-end overview of our approach to bridging the sim-to-real gap, leveraging domain randomization and careful choices in control architecture in order to successfully deploy RL policies for teleoperation in simulation and on hardware