MITIGATING REALITY GAP THROUGH TRAINING A SIMULATION-TO-REAL MODEL USING A VISION-BASED ROBOT TASK MODEL

摘要

Implementations disclosed herein relate to mitigating the reality gap through training a simulation-to-real machine learning model ("Sim2Real" model) using a vision-based robot task machine learning model. The vision-based robot task machine learning model can be, for example, a reinforcement learning ("RL") neural network model (RL-network), such as an RL-network that represents a Q-function.