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This paper introduces the Assistive Kitchen as a comprehensive demonstration and challenge scenario for technical cognitive systems. We describe its hardware and software infrastructure. Within the Assistive Kitchen application, we select particular domain activities as research subjects and identify the cognitive capabilities needed for perceiving, interpreting, analyzing, and executing these activities as research foci. We conclude by outlining open research issues that need to be solved to realize the scenarios successfully.

Abstract-In this paper we investigate the acquisition of 3D functional object maps for indoor household environments, in particular kitchens, out of 3D point cloud data. By modeling the static objects in the world into hierarchical classes in the map, such as cupboards, tables, drawers, and kitchen appliances, we create a library of objects which a household robotic assistant can use while performing its tasks.Our method takes a complete 3D point cloud model as input, and computes an object model for it. The objects have states (such as open and closed), and the resulted model is accurate enough to use it in physics-based simulations, where the doors can be opened based on their hinge position. The model is built through a series of geometrical reasoning steps, namely: planar segmentation, cuboid decomposition, fixture recognition and interpretation (e.g. handles and knobs), and object classification based on object state information.

In everyday object manipulation tasks, like making a pancake, autonomous robots are required to decide on the appropriate action parametrizations in order to achieve desired (and to avoid undesired) outcomes. For determining the right parameters for actions like pouring a pancake mix onto a pancake maker, robots need capabilities to predict the physical consequences of their own manipulation actions. In this work, we integrate a simulation-based approach for making temporal projections for robot manipulation actions into the logic programming language PROLOG. The realized system enables robots to determine action parameters that bring about certain effects by utilizing simulation-based temporal projections within PROLOG's chronological backtracking mechanism. For a set of formal parameters and their respective ranges of values, the developed system translates the manipulation problems into physical simulations, monitors and logs the relevant data structures of the simulations, translates the logged data back into first-order time-interval-based representations, called timelines, and eventually evaluates the individual timelines with respect to specified performance criteria. Integrating the proposed approach into robot control programs allow robots to mentally simulate the consequences of different action parametrizations before committing to them and thereby to reduce the number of undesired outcomes.