It is hard for a machine to differentiate between features that belong to a scene the robot has already seen and a new scene that happens to contain some of the same objects.
Robots can only perform limited reasoning due to the fact that their computations are carefully specified. For today’s robots, everything is spelled out with simple instructions and the scope of the robot is entirely contained in its program. Tasks that humans take for granted, for example asking the question “Have I been here before?” are notoriously difficult for robots. Robots record the features of the places they have visited. These features are extracted from sensors such as cameras or laser scanners. It is hard for a machine to differentiate between features that belong to a scene the robot has already seen and a new scene that happens to contain some of the same objects. In general, the data collected from sensors and actuators is too big and too low level; it needs to be mapped to meaningful abstractions for robots to be able to effectively use the information. Current machine-learning research on big data is addressing how to compress a large dataset to a small number of semantically meaningful data points. Summarization can also be used by robots. For example, robots could summarize their visual history to reduce significantly the number of images required to determine whether “I have been here before.”