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Cognitive Hierarchies: A Principled Approach to Cognitive Robotics

Many modern robotic applications call for robots that can exhibit highly adaptive behaviours. For example, the development of domestic robotics requires a robot that can interact in unstructured environments with both human and non-human agents. The robot must be able to perform abstract reasoning about its environment, its goals, and the goals of other agents. Crucially, such high-level reasoning needs to occur with respect to the sensing, motion, and computational capabilities of the robot itself. To date, most approaches to building such complex robotic systems have been largely ad-hoc; using software frameworks to plug together components that implement algorithms for vision, mapping, navigation, manipulation, and reasoning. The result is that, while the capabilities of the individual components may be well understood, the system as a whole is poor specified and understood, often resulting in brittle and unpredictable behaviour. A more principled approach is required.

In this talk I will introduce a framework for integrating disparate representations within a robotic system. The framework is formalised as an abstract meta-theory consisting of nodes in a hierarchy, with each node encoding a sub-system that maintains its own belief-state and generates behaviour. Nodes are formalised abstractly, with the representational details of individual nodes remaining opaque at the framework level, thus allowing for the integration of different representations and reasoning mechanisms. For example, connecting a node that performs purely symbolic reasoning with a purely probabilistic node. Due to the formal nature of this framework it can be used to establish meta-theoretic properties as well as to prove properties of a particular hierarchical instantiation. I argue that using more formal frameworks, such as the framework proposed in this talk, is a critical component if we are to build adaptive, complex robots that can operate effectively in unstructured environments.