Representation Learning in Partially Observable Environments using Sensorimotor Prediction

Autonomous Learning for Robotics aims to endow (robotic) agents with the capability to learn from and act in their environment, so that it can adapt to previously unseen situations. In order to be able to learn from this interaction, an agent has to build compact representations of the environments, using information captured from a high dimensional raw input. Current approaches favor the learning of representations using Deep Neural Networks ([1], [2], [3]). Supervised learning extracts representations from the data to solve a classification task, providing the agent with hierarchical compact representations of different sensory streams ([4], [5]). However, these state-of-the-art machine learning algorithms are not suitable for autonomous learning, as they rely on labeled data, which are costly to acquire, and are constraining the representations on the classes they were trained on. Unsupervised learning allows to learn hierarchical compression for different data streams ([6], [7], [8]).