ADAPT: A Cognitive Architecture for Robotics (original) (raw)
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aaai.org
We are implementing ADAPT, a cognitive architecture for a Pioneer mobile robot, to give the robot the full range of cognitive abilities including perception, use of natural language, learning and the ability to solve complex problems. Our perspective is that an architecture based on a unified theory of robot cognition has the best chance of attaining human-level performance. Existing work in cognitive modeling has accomplished much in the construction of such unified cognitive architectures in areas other than robotics; however, there are major respects in which these architectures are inadequate for robot cognition. This paper examines two major inadequacies of current cognitive architectures for robotics: the absence of support for true concurrency and for active perception. ADAPT models the world as a network of concurrent processes, and models perception as problem solving. ADAPT integrates three theories: the theory of cognition embodied in the Soar system, the RS formal model of concurrent sensorimotor activity and an algebraic theory of decomposition and reformulation. These three component theories have been implemented and tested separately and their integration is currently underway. This paper describes these components and the plan for their integration.
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Nowadays, robots have to face very complex tasks, often requiring collaboration between several individuals. As a consequence, robotics can be considered one of the most suitable paradigms for agent-based software. In this work, we present an approach to the design of distributed multi-agent architectures for mobile robotics, that is based on the Unified Modeling Language.
Proceedings of SPIE, 2009
Our long-term goal is to develop autonomous robotic systems that have the cognitive abilities of humans, including communication, coordination, adapting to novel situations, and learning through experience. Our approach rests on the recent integration of the Soar cognitive architecture with both virtual and physical robotic systems. Soar has been used to develop a wide variety of knowledge-rich agents for complex virtual environments, including distributed training environments and interactive computer games. For development and testing in robotic virtual environments, Soar interfaces to a variety of robotic simulators and a simple mobile robot. We have recently made significant extensions to Soar that add new memories and new non-symbolic reasoning to Soar's original symbolic processing, which should significantly improve Soar abilities for control of robots. These extensions include episodic memory, semantic memory, reinforcement learning, and mental imagery. Episodic memory and semantic memory support the learning and recalling of prior events and situations as well as facts about the world. Reinforcement learning provides the ability of the system to tune its procedural knowledgeknowledge about how to do things. Mental imagery supports the use of diagrammatic and visual representations that are critical to support spatial reasoning. We speculate on the future of unmanned systems and the need for cognitive robotics to support dynamic instruction and taskability.
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