D2.1 Requirements and Specification - CORBYS

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D2.1 Requirements and Specification unable to perform the job in a timely fashion. The system would produce a plan, but before this plan could be executed in full, it often became invalidated by changes in the real-world. Because of the inability of SPA architectures to perform real-world operations, researchers searched for a robotic control method that did not rely on high level reasoning so that the school of reactive and behavioural robotics emerged. This approach basically tries to produce “intelligent behaviour” driven by sensory data, the robot reacts intelligently to what it senses (Brooks 1990). Architectures from the reactive and behavioural robotics school were typified in their rejection of a symbolic representation of the outside world, along with a bottom-up approach to achieving complex behaviours (Ross, 2004). Thus, on the one hand SPA robotics failed to produce good experimental results because the level of planning and other cognitive tasks attempted was too complex to cope with in a real-world environment. On the other hand reactive robotics suffered in another way, in that only immediate reactive actions could be performed but not complex tasks which needed to be ’thought about’ ahead of their execution as the cognitive functions were not supported. The natural progression in the design of a robot control architecture was therefore the development of architectures comprising of both reactive and deliberative components. Therefore this overview only concerns architectures which are hybrid in nature. The hybrid cognitive architectures are mainly characterised by a layering of capabilities where low-level layers provide reactive capabilities, supporting fast perception, control and task execution on a low-level and high-level layers provide the more computationally intensive deliberative capabilities including symbolic reasoning as a necessity for recognition and interpretation of complex contexts, planning of intrinsic tasks, and learning of behaviours. In such a layered architecture, a robot’s control subsystems are arranged into a hierarchy with higher layers dealing with information at increasing levels of abstraction. A key problem in such architectures is what kind of control framework to embed to manage the interactions between the various layers. There exists: Horizontal layering-where layers are each directly connected to the sensory input and action output. In effect, each layer itself acts like an agent, producing suggestions as to what action to perform. Vertical layering-Sensory input and action output are each dealt with by at most one layer each Typical three layers architecture based on vertical decomposition of components that supports cognitive functions is illustrated in Figure 28. Figure 28: The illustration of typical three layers architecture Classic example of three layered architecture is ATLANTIS (Gat, 1997) where the above illustrated three layers have the following functionalities: The Controller: collection of reactive “behaviours” where each behaviour is fast and has minimal internal state The Sequencer: decides which primitive behaviour to run next; does not do anything that takes a long 134 .
D2.1 Requirements and Specification time to compute, because the next behaviour must be specified soon The Deliberator: slow but smart; can either produce plans for the sequencer, or respond to queries from it The majority of architectures to follow Gat’s architecture adopt the basic idea of layered architecture but adapt it to meet the challenges of complex robotic systems. Also, it should be noted that not all architectures are equally hybrid, with many hybrid architectures choosing to omit an explicit planning layer in favour of using plan libraries or a behaviour sequencer. These will be illustrated in the next section where several preeminent examples are analysed aiming to identify core elements which are essential to providing intelligent robot control architecture. 13.2 Cognitive architectures used for controlling different robotic systems 13.2.1 Armar – a cognitive architecture for a humanoid robot In order to develop architecture that supports fast perception, control and task execution on a low-level as well as recognition and interpretation of complex contexts, planning of tasks execution, and learning of behaviours at a high-level (Burghart et al.2005) chose a three-layered architecture Armar adapted to the requirements of a humanoid robot. It is a mixture of a hierarchical three-layered form on the one hand and a composition of behaviour-specific modules on the other hand as described in (Vernon et al. 2006). 135
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CORBYS Cognitive Control Framework
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D2.1 Requirements and Specification - CORBYS

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