ABSTRACT. Distributed architectures for implementing tasks on humanoid robots is a design challenge, both in theory and practice. Although important functionality resides within the component modules of the system, the performance of the middleware – the software for mediating information between modules – is critical to overall system performance. We have designed an architecture serving various functional roles and information exchange within a distributed system, using three different communication subsystems: the Cognitive Map (CogMap), Distributed Operation via Discrete Events (DiODE), and Multimodal Communication (MC). The CogMap is implemented in Psyclone, a framework for constructing large AI systems, and allows sharing and transformation of information streams dynamically between modules. DiODE provides a direct connection between two modules while MC implements a multi-modal server that streams raw sensory data to requesting external (off-board) perceptual modules. These have been implemented and tested on the Honda Motor Corporation's ASIMO humanoid robot. To identify trade-offs and understand performance limitations in robots with distributed system architectures, we performed a variety of tests on these subsystems under different network conditions, operating systems and computational loads. The results indicate that delays due to our middleware is negligible compared to computational costs associated with actual processing within the modules, provided a network with high enough bandwidth. The Cognitive Map appears to be scalable to an increasing number of connected modules with negligible degradation of package delays.

ABSTRACT. Many humanoid robots like ASIMO are built to potentially perform more than one type of task. However, the need to maintain a consistent physical appearance of the robot restricts the installation of additional sensors or appendages that would alter its visual identity. Limited battery power for free-moving locomotive robots places temporal and spacial complexity limits on the algorithms we can deploy on the robot. With these conditions in mind, we have developed a distributed robot architecture that combines onboard functionality with external system modules to perform tasks involving interaction with the environment. An information model called the Cognitive Map organizes output produced by multiple perceptual modules and presents a common abstraction interface for other modules to access the information. For the planning and generation of motion on the robot, the Task Matrix embodies a task abstraction model that maps a high level task description into its primitive motions realizable on the robot. Our architecture supports different control paradigms and information models that can be tailored for specific tasks. We demonstrate environmental tasks we implemented with our system, such as pointing at moving objects and pushing an object around a table in simulation and on the actual ASIMO
robot.

ABSTRACT. This paper compares two solutions for human-like perception using two different modular plug-and-play frameworks, CAVIAR (List et al, 2005) and Psyclone (Thórisson et al, 2004, 2005a). Each uses a central point of configuration and requires the modules to be auto-descriptive, auto-critical and auto-regulative (Crowley and Reignier, 2003) for fully autonomous configuration of processing and dataflow. This allows new modules to be added to or removed from the system with minimal reconfiguration. CAVIAR uses a centralised global controller (Bins et al, 2005) whereas Psyclone is a fully distributed control architecture. The differences between the two frameworks result in very different solutions to control issues such as dataflow regulation and module substitution. We implemented a computer vision-based human behaviour tracker for public scenes in both frameworks and found that in both it was easy to gradually develop a modular architecture with increasing functionality and to add new and competing modules. CAVIAR’s global controller uses offline learned knowledge to regulate module parameters and select between competing results whereas in Psyclone dynamic multi-level control modules adjust parameters, data and process flow.