Expanding task functionality in established humanoid robots

摘要

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.