Two Perspectives on Learning Rich Representations from Robot Experience

This position paper describes two approaches towards the representations that a robot can learn from its experience. In the first approach, the robot learns models for reasoning about human-interpretable aspects of the environment, for example models of space and objects. In the second approach, the robot incrementally learns predictions for the consequences of performing policies, where a policy is any experimental procedure that the robot can perform. These two approaches correspond closely to the ideas of a scientific model and an experimental prediction, and ideally the benefits of both can be accessible to a robot.