An Examination of the Static to Dynamic Imitation Spectrum

Abstract

We consider the issues that arise from an examination of the continuum between two social learning paradigms that are widely used in robotics research: (i) following or matched-dependent behaviour and (ii) static observational learning. We use physical robots with minimal sensory capabilities and exploit controllers using neural network based methods for agent-centred perception of model angle and distance. The robot is first trained to perceive the dynamic movement of a robot model carrying a light source, then the robot learns by observing the model demonstrate a behaviour and finally it attempts to re-enact the learnt behaviour. Our results indicate that a dynamic observation using rotation performs significantly better than static observation. However given the embodiment of the robot a dynamic strategy using both rotational and translational movement becomes more problematic. We give reasons for this, discuss lessons learned for combining these types of social learning and make suggestions for requirements for imitator robots using dynamic observation.