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Continual learning (CL) is a particular machine learning paradigm where thedata distribution and learning objective changes through time, or where all thetraining data and objective criteria are never available at once. The evolutionof the learning process is modeled by a sequence of learning experiences wherethe goal is to be able to learn new skills all along the sequence withoutforgetting what has been previously learned. Continual learning also aims atthe same time at optimizing the memory, the computation power and the speedduring the learning process.  An important challenge for machine learning is not necessarily findingsolutions that work in the real world but rather finding stable algorithms thatcan learn in real world. Hence, the ideal approach would be tackling the realworld in a embodied platform: an autonomous agent. Continual learning wouldthen be effective in an autonomous agent or robot, which would learnautonomously through time about the external world, and incrementally develop aset of complex skills and knowledge.  Robotic agents have to learn to adapt and interact with their environmentusing a continuous stream of observations. Some recent approaches aim attackling continual learning for robotics, but most recent papers on continuallearning only experiment approaches in simulation or with static datasets.Unfortunately, the evaluation of those algorithms does not provide insights onwhether their solutions may help continual learning in the context of robotics.This paper aims at reviewing the existing state of the art of continuallearning, summarizing existing benchmarks and metrics, and proposing aframework for presenting and evaluating both robotics and non roboticsapproaches in a way that makes transfer between both fields easier.

Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges