One of the objectives of Cognitive Robotics is to construct robot systems that can be directed to achieve realworld goals by high-level directions rather than complex, low-level robot programming. Such a system must have the ability to represent, problem-solve and learn about its environment as well as communicate with other agents. In previous work, we have proposed ADAPT, a Cognitive Architecture that views perception as top-down and goaloriented and part of the problem solving process. Our approach is linked to a SOAR-based problem-solving and learning framework. In this paper, we present an architecture for the perceptive and world modelling components of ADAPT and report on experimental results using this architecture to predict complex object behaviour.A novel aspect of our approach is a 'mirror system' that ensures that the modelled background and foreground objects are synchronized with observations and task-based expectations. This is based on our prior work on comparing real and synthetic images. We show results for a moving object that collides and rebounds from its environment, hence showing that this perception-based problem solving approach has the potential to be used to predict complex object motions.

A mobile robot moving in an environment in which there are other moving objects and active agents, some of which may represent threats and some of which may represent collaborators, needs to be able to reason about the potential future behaviors of those objects and agents. In previous work, we presented an approach to tracking targets with complex behavior, leveraging a 3D simulation engine to generate predicted imagery and comparing that against real imagery. We introduced an approach to compare real and simulated imagery using an affine image transformation that maps the real scene to the synthetic scene in a robust fashion.In this paper, we present an approach to continually synchronize the real and synthetic video by mapping the affine transformation yielded by the real/synthetic image comparison to a new pose for the synthetic camera. We show a series of results for pairs of real and synthetic scenes containing objects including similar and different scenes.