D2.1 Requirements and Specification - CORBYS

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D2.1 Requirements and Specification environment. In fact, entities therein only become semantically useful objects through the actions the robot performs on. Real-time control: Fast reactive components (low-level) and building blocks for recognition or task coordination (midlevel) act in real-time. Active models used for real-time perceptions and tasks are retrieved from the global knowledge database and stored in a cache memory. In addition, they have a certain a degree of autonomy for adaptation. The execution supervisor is responsible for the priority management of tasks and perceptual components. 13.2.2 ISAC: IMA (Intelligent Machine Architecture) The IMA (Intelligent Machine Architecture) was specifically developed to ease the integration of heterogeneous software components. The general premise is that all software components can be modelled as basic agents which are loosely coupled over a DCOM (Distributed Component object model) connection. Here the application of multi-agent based hybrid cognitive architecture IMA in the humanoid robot ISAC where all aspects of the robots control system are modelled around agents is presented. It is implemented with symbolic components (software agents) which embed different connectionist algorithms. The layout of IMA cognitive architecture is depicted on Figure 31. Figure 31: IMA multiagent-based cognitive robot architecture (Kawamura et.al. 2004) Robotic platform (description, actuation and sensing): ISAC is an upper body part humanoid robot that has six DoF arms, pneumatically actuated by agonist/antagonist McKibben muscles. Each arm has four finger and thumb. ISAC is equipped with an active stereovision system, laser motion detectors, stereo microphones to localise the position of the sound source, touch sensors on fingers, proximity sensors on palms and two force sensors between hands and arms. System (software) architecture: The IMA architecture is collection of software modules (agents). Each IMA agent encapsulates all aspects of 138
D2.1 Requirements and Specification single element (logical or physical) of a hardware component, computational task or data. Software agents communicate through asynchronous message passing. The agents are implemented as atomic or compound agents. Atomic agents encapsulate a single resource and it does not depend on other agents. Compound agents contain or depend on other agents for their primary function. Sensing and Perception In the IMA architecture each stimulus is processed by a different perception agent (different processing techniques have been used for different sensors). To each sensor an IMA agent that processes sensor information and stores the result in short-term memory is assigned. For example, there are separate visual agents that perform recognition of objects, object localisation, face recognition and etc. The modular approach makes it possible to add additional sensors. Human-robot Interaction: The two software agents are responsible for HRI (Kawamura et al., 2000), one of them is human-agent that encapsulates information that robot determined about human and other is self-agent that addresses the humanoid’s cognitive aspects. Real-time control: The initial usage of IMA architecture in the ISAC caused problems with real-time control (Kawamura et. al. 2004). Asynchronous message passing has caused latency in passing control inputs to real-time controllers. Therefore, separate encapsulation of multi-agent tasks (such as visual serving) has been carried out. Head, arm and hand agents are responsible for controlling the head, arm and hand actuators. They accept commands from one or more clients and carry out command arbitration. The agents also provide the clients with information about its current state (for example joint position). The actuation agents pass referent joint angles and velocities to servo controllers. The servo control loops have been realised using a QNX real-time operating system. Planning: The self-agent is responsible for planning actions of the robot. The most important modules of the Self-agent are Central-Executive-Agent (CEA) and First-Order-Response (FOR). CEA module makes decisions and invokes skills necessary for performing the given task that has been selected by Intention-Agent based on perceived sensory information. The FOR module is responsible for creating the reactive responses of the robot. Learning: The system is equipped with different types of memories in order to support learning. The memory structure is modular and divided into three components: short-term memory, long-term memory and working memory. Short-term memory holds most recent sensory information on the current environment in which ISAC robot operates. Long-term memory holds learned behaviours, semantic knowledge and past experience and it has three structures: semantic, procedural and episodic. Working-memory holds task-specific information that authors call “chunks”. The working-memory uses temporal difference learning algorithms and neural network to provide learning in IMA architecture. Communication: The communication between agents is based on asynchronous message passing. Any agent can be accessed by any other agent. By increasing the number of agents the system faced with communication “lock-ups” 13.2.3 iCub The cognitive architecture of iCub robot is depicted in Figure 33. The architecture “..comprises a network of 139
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D2.1 Requirements and Specification - CORBYS

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