Open-Sourcing Generative Models for Data-driven Robot Simulations

Open-source robot hardware has become popular in recent years due easy and low-cost fabrication with 3D printing. Applying reinforcement learning algorithms to these robots, however, require the collection of a large amount of data during robot execution. The process is time consuming and can damage the robot. In addition, data collected for one robot may not be applicable for a similar one due to inherent uncertainties (e.g., friction, compliance, etc.) in the fabrication process. Therefore, we propose to disseminate a generative model rather than actual recorded data. We propose to use a limited amount of real data on a robot to train a Generative Adversarial Network (GAN). We show on two robotic systems that training a regression model using generated synthetic data provides transition accuracy at least as good as real data. Such model could be open-sourced along with the hardware to provide easy and rapid access to research platforms.