CREATE: Multimodal Dataset for Unsupervised Learning, Generative Modeling and Prediction of Sensory Data from a Mobile Robot in Indoor Environments
This provides a resource for researchers in robotics and AI to study multimodal learning, but it is incremental as it focuses on dataset creation without new algorithmic advances.
The authors introduced the CREATE dataset, comprising 14 hours of multimodal sensory recordings from a mobile robot in indoor environments, to address the problem of learning multimodal representations and dependencies for tasks like unsupervised object learning and prediction.
The CREATE database is composed of 14 hours of multimodal recordings from a mobile robotic platform based on the iRobot Create. The various sensors cover vision, audition, motors and proprioception. The dataset has been designed in the context of a mobile robot that can learn multimodal representations of its environment, thanks to its ability to navigate the environment. This ability can also be used to learn the dependencies and relationships between the different modalities of the robot (e.g. vision, audition), as they reflect both the external environment and the internal state of the robot. The provided multimodal dataset is expected to have multiple usages, such as multimodal unsupervised object learning, multimodal prediction and egomotion/causality detection.