Artificial General Intelligence (AGI) aims to create agents that can learn how to interact with their environment by themselves. In order to act in an environment, an agent needs to perceive its surroundings. Historically, researchers in Robotics and Reinforcement Learning have relied on either hand-designed sensory representations or detection and classification algorithms to detect and represent what is in the vicinity of the agent. However, these rely on biased information provided by the human experimenters.
We want to go beyond classical approaches and propose to learn these representations. This field, named State Representation Learning, benefits from the advances in Deep Learning (DL) and Deep Reinforcement Learning (Deep RL) and allows to leverage sensorimotor information to learn these representations in a self-supervised way. By building a predictive model of how an agent’s sensors will be influenced by its motor actions, we can build abstract representations and learn common sense knowledge, naïve physics, as well as representation of the agent’s own body and the space around it. The goal of this project is to endow an agent with the mechanisms that allow the learning of these representations. We have an end-to-end approach where an agent’s cognitive architecture is modeled using Deep Neural Networks, taking inspiration from different scientific fields, notably Neuroscience and Cognitive Neuroscience.
Laflaquiere, A. and Garcia Ortiz, M. (2019). Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox and R. Garnett (Eds.), Proceedings of the 33rd Conference on Neural Information Processing Systems (NeurIPS 2019), 8-14 December, Vancouver, Canada. Advances in Neural Information Processing Systems, 32.Garcia Ortiz, M. and Laflaquiere, A. (2018). Learning Representations of Spatial Displacement through Sensorimotor Prediction. In Proceedings of the Joint IEEE 8th International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob 2018), (pp. 7-12), 17 September, Tokyo, Japan. doi: 10.1109/DEVLRN.2018.8761034.