A dataset for robots refers to a collection of structured data used to train, test, and validate machine learning algorithms and artificial intelligence systems for robotic applications. These datasets are essential for developing robots that can perceive their environment, make decisions, and perform different tasks. 

There are different types of data: visual like 3D scans, RGB images (these are true colour images stored in a specific way that defines red, green, and blue colour components for each individual pixel. A pixel is the smallest element in an image and it can be manipulated through software.), sensor readings (e.g., data from cameras), annotated information (e.g., labels for objects and scenes), and natural language data (e.g., dialogues).

Many datasets use simulated environments to create large-scale, diverse data collections without the need for physical robots.

Datasets can be shaped for tasks, like object recognition, navigation and natural language understanding.

Typically, datasets are divided into training, testing, and validation sets. 

Building high-quality datasets for robots is challenging as they capture real-world complexities such as varying lighting conditions and cluttered environments. These real-world conditions are easy to deal with for humans, as our brains can easily recognize objects even if they stand upside down, are wrongly coloured, or put into specific light conditions, for example. They aren’t as easy to cope with for machines.