A Forward / Inverse Motor Controller for Cognitive Robotics

International Journal of Humanoid Robotics, 2004

Described is the parallel distributed cognitive control architecture and the advantages and limitations that have guided its development. Primary structures for sensing, memory, and cognition are described. Motion learning through teleoperation and fault diagnosis through system health monitoring are described. The generality of the control system is discussed in terms of its applicability to physically heterogeneous robots and multi-robot systems.

Innovations in Applied Artificial Intelligence, 2004

The Cognitive Controller (CoCo) is a new, three-tiered control architecture for autonomous agents that combines reactive and deliberative components. A behaviour-based reactive module ensures that the agent can gracefully handle the various real-time challenges of its environment, while a logic-based deliberative module endows the agent with the ability to ���think ahead���, performing more complex high-level tasks that require planning. A plan execution and monitoring module establishes an advisor-client relationship between the ...

Cognitive Processing, 2011

Robots are increasingly expected to perform tasks in complex environments. To this end, engineers provide them with processing architectures that are based on models of human information processing. In contrast to traditional models, where information processing is typically set up in stages (i.e., from perception to cognition to action), it is increasingly acknowledged by psychologists and robot engineers that perception and action are parts of an interactive and integrated process. In this paper, we present HiTEC, a novel computational (cognitive) model that allows for direct interaction between perception and action as well as for cognitive control, demonstrated by task-related attentional influences. Simulation results show that key behavioral studies can be readily replicated. Three processing aspects of HiTEC are stressed for their importance for cognitive robotics: (1) ideomotor learning of action control, (2) the influence of task context and attention on perception, action planning, and learning, and (3) the interaction between perception and action planning. Implications for the design of cognitive robotics are discussed.

cosy.informatik.uni-bremen.de

Cognitive robotics is the research field at the confluence of Artificial Intelligence and robotics. Its goal is to program robotic agents using explicitly only high-level actions and relations among actions characterized as formal logical statements. The advantages of cognitive robotics when compared with traditional robot programming are (i) the possibility of formal verification of expected properties and behaviors of robotic agents, and (ii) the capability to program robots for highly complex tasks in information-rich environments, including tasks and environments that require communication, coordination and cooperation among robots. A common barrier to work with cognitive robotics-and autonomous robots in general-is the high cost of robots. Low cost platforms such as Lego R MindStorms TM are usually considered useful only for teaching applications, and not for research prototyping and experimentation. In the present article we show how a Lego R MindStorms TM robot can be used to implement and run an experiment in which several Artificial Intelligence techniques are required.

Cognitive Processing, 2011

2006 World Automation Congress, 2006

This paper describes our efforts to develop a robot with robust sensorimotor intelligence using a multiagent-based robot control architecture and a biologically inspired intelligent control. Such control is called cognitive control. In this paper we will discuss the application of cognitive control to a humanoid robot. Features of cognitive control addressed include short-term memory for environmental learning, long-term memory for behavior learning and task execution using working memory and TD learning.

… and Automation, 2005. …, 2005

This paper deals with building up a knowledge base of manipulation tasks by extracting relevant knowledge from demonstrations of manipulation problems. Hereby the focus of the paper is on modeling and representing manipulation tasks enabling the system to reason and reorganize the gathered knowledge in terms of reusability, scalability and explainability of learned skills and tasks. The goal is to compare the newly acquired skill or task with already existing task knowledge and decide whether to add a new task representation or to expand the existing representation with an alternative. Furthermore, a constraint for the representation is that at execution time the built knowledge base can be integrated and used in a symbolic planner.

IEEE International Conference Mechatronics and Automation, 2005, 2005

Robot skills that are needed to operate completely autonomously in a real complex scenario populated by humans are still beyond the capabilities that the current robotic technology offers. In spite of that, in a variety of assistant applications where human and robot are closely tied, mobile robots can perform an effective and valuable work if humans in the surroundings (or the assisted person) would be enabled to extend the robot abilities through skills either not supported by the machine or supported in a different and (maybe) less dependable manner. To achieve that, robot and human must closely interact and collaborate at all levels of the robotic architecture, including deliberation, control and execution. This paper proposes a new robotic architecture, called ACHRIN, which supports a strong integration of the human into the robotic system in order to improve the overall performance of the robot as well as its dependability. Our human-robot integration relies to a great extend on sharing symbolic concepts of the world (cognitive integration). ACHRIN has been implemented and tested in a real rehabilitation robot: a robotic wheelchair that provides mobility to impaired or elderly people.

SPIE Proceedings, 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.