Rethinking Continual Learning for Autonomous Agents and Robots

Abstract

Continual learning refers to the ability of a biological or artificial system to seamlessly learn from continuous streams of information while preventing catastrophic forgetting, i.e., a condition in which new incoming information strongly interferes with previously learned representations. Since it is unrealistic to provide artificial agents with all the necessary prior knowledge to effectively operate in real-world conditions, they must exhibit a rich set of learning capabilities enabling them to interact in complex environments with the aim to process and make sense of continuous streams of (often uncertain) information. While the vast majority of continual learning models are designed to alleviate catastrophic forgetting on simplified classification tasks, here we focus on continual learning for autonomous agents and robots required to operate in much more challenging experimental settings. In particular, we discuss well-established biological learning factors such as developmental and curriculum learning, transfer learning, and intrinsic motivation and their computational counterparts for modeling the progressive acquisition of increasingly complex knowledge and skills in a continual fashion.