Robotic Datasets

Robotic datasets are collections of sensory data (images, depth maps, lidar, IMU data, etc.) and corresponding robot actions, crucial for training and evaluating robot learning algorithms. Current research emphasizes creating larger, more diverse datasets encompassing various robot platforms, tasks, and environments, often employing techniques like data augmentation and domain adaptation to address data scarcity and improve generalization. This work is driven by the need for robust, generalizable robot policies, leveraging architectures such as transformers and employing algorithms like distributionally robust optimization to improve data efficiency and downstream performance in applications ranging from manipulation to navigation. The resulting datasets and improved algorithms are vital for advancing the field of robotics and enabling more capable and adaptable robots in real-world scenarios.