Robot behaviour learning using Topological Gaussian Adaptive Resonance Hidden Markov Model

Behaviour learning by robots is an emerging research field. The robot behaviour learning in realistic environment requires the robot to be able to learn incrementally in order to adapt itself to the dynamic environment. The incremental learning system should be stable and acquire new information without corrupting previous knowledge. Probabilistic approach has become the prevalent epitome in robotic learning systems. In probabilistic learning models, such as Hidden Markov Model, estimating the optimal number of states is difficult which limits the models capabilities by limiting the number of motion patterns to be learned a priori. In this paper, we presented an approach for learning behaviour patterns through continuous observation. We have proposed a novel architecture for learning the spatio-temporal sequences using Topological Gaussian Adaptive Resonance Hidden Markov Model. The proposed model dynamically generates the graph-based structure for the observed patterns through a novel topological mapping architecture. The structure (number of states) of the probabilistic model is not fixed and is adjusted based on the topological map of the acquired motion features. The topological map consists of nodes connected with edges, where each node represents the encoded motion features to adaptively generalize the observed behaviour patterns. The model combines the self-organizing, self-stabilizing properties and time series processing features to learn the spatio-temporal sequences. To demonstrate the properties of the proposed algorithm, we have performed a set of experiments through simulation on DARwIn-OP humanoid robot.