Simulations and synthetic datasets have historically empower the research in different service robotics-related problems, being revamped nowadays with the utilization of rich virtual environments. However, with their use, special attention must be paid so the resulting algorithms are not biased by the synthetic data and can generalize to real world conditions. These aspects are usually compromised when the virtual environments are manually designed. This article presents Robot@VirtualHome, an ecosystem of virtual environments and tools that allows for the management of realistic virtual environments where robotic simulations can be performed. Here “realistic” means that those environments have been designed by mimicking the rooms’ layout and objects appearing in 30 real houses, hence not being influenced by the designer’s knowledge. The provided virtual environments are highly customizable (lighting conditions, textures, objects’ models, etc.), accommodate meta-information about the elements appearing therein (objects’ types, room categories and layouts, etc.), and support the inclusion of virtual service robots and sensors. To illustrate the possibilities of Robot@VirtualHome we show how it has been used to collect a synthetic dataset, and also exemplify how to exploit it to successfully face two service robotics-related problems: semantic mapping and appearance-based localization.
