Unsupervised Temporospatial Neural Architecture for Sensorimotor Map Learning

The ability to learn the sensorimotor maps of unknown environments without supervision is a vital capability of any autonomous agent, be it biological or artificial. An accurate sensorimotor map should be able to encode the agents world and equip it with the capability to anticipate or predict the results of its actions. However, to design a robust autonomous learning technique for an unknown, dynamic, partially observable or noisy environment remains a daunting task. This paper proposes a Temporospatial Merge Grow When Required (TMGWR) network for continuous self-organisation of an agents sensorimotor awareness in noisy environments. TMGWR is an adaptive neural algorithm that learns the sensorimotor map of an agent’s world using a time series self-organising strategy and the Grow When Required (GWR) algorithm. The algorithm is compared with GNG, GWR and TGNG in terms of their disambiguation performance, sensorial representation accuracy and sensorimotor-link error, a new metric that is developed in this paper to evaluate how well a sensorimotor map represents causality in the agents world. The outcomes of the experiments show that TMGWR is more efficient and suitable for sensorimotor map learning in noisy environments than the competing algorithms.