Abstract

Real world robotics is a multifarious process spanning several fields including simulation, semantic/scene understanding, reinforcement learning, domain randomization, just to name a few. Ideally simulators would accurately capture the real world perfectly in a much faster capacity allowing for a predictive power of how a robot will interact with its environment. Unfortunately, simulators neither have the speed nor accuracy to support this. Simulators, such as Gazebo, Webots, and OpenRave, are supplemented with machine learned models of their environment to solve specific tasks such as scene understanding and path planning. This can be compared to a physical only solution which can be costly in terms of price and time. Advances in virtual reality allow for new ways for humans to provide training data for robotic systems in simulation. Using modern datasets such as SUNCG and Matterport3D we now have more ability than ever to train robots in virtual environments. Through understanding modern applications of simulations, better robotic platforms can be designed to solve some of the most pressing challenges of modern robotics.

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