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Cognitive robotics is concerned with endowing a robot with intelligent behavior by providing it with a processing architecture that will allow it to learn and reason about how to behave in response to complex goals in a complex world. Cognitive robotics may be considered the engineering branch of embodied cognitive science and embodied embedded cognition.
While traditional cognitive modeling approaches have assumed symbolic coding schemes as a means for depicting the world, translating the world into these kinds of symbolic representations has proven to be problematic if not untenable. Perception and action and the notion of symbolic representation are therefore core issues to be addressed in cognitive robotics.
Cognitive robotics views animal cognition as a starting point for the development of robotic information processing, as opposed to more traditional Artificial Intelligence techniques. Target robotic cognitive capabilities include perception processing, attention allocation, anticipation, planning, complex motor coordination, reasoning about other agents and perhaps even about their own mental states. Robotic cognition embodies the behavior of intelligent agents in the physical world (or a virtual world, in the case of simulated cognitive robotics). Ultimately the robot must be able to act in the real world.
One of the learning techniques that are used for robots is learning by imitation: the robot, provided with all the sensors and physical hardware needed to perform a human task, is monitoring the human performing a task, and then the robot tries to imitate the same movements that the human performed in order to achieve the task. Using its sensors, the robot should be able to create a three-dimensional image of the environment, and to recognize the objects in that image. A major challenge is hence to interpret the scene, and to understand what objects are needed in the task and which are not.
A more complex learning approach is autonomous knowledge acquisition: the robot now uses its sensors and its knowledge about the physical properties of the world, and is then left to explore the environment on its own. One of the terminologies of this behavior is called motor babbling. The idea of this approach is to let the robot discover its capabilities on its own.
A somewhat more directed mode of exploration can be achieved by "curiosity" algorithms, such as Intelligent Adaptive Curiosity or Category-Based Intrinsic Motivation. These algorithms generally involve breaking sensory input into a finite number of categories and assigning some sort of prediction system (such as an Artificial Neural Network) to each. The prediction system predicts sensor values for the next time point and keeps track of the error in its predictions over time. Reduction in prediction error can be seen as analogous to learning. The robot then preferentially explores categories in which it is learning (i.e. reducing prediction error) the fastest. This enables the robot to learn easier things first before moving on to those that it may or may not be able to learn.
Some researchers in cognitive robotics have begun using architectures such as (ACT-R and Soar (cognitive architecture)) as a basis of their cognitive robotics programs. These architectures have been successfully used to simulate operator performance and human performance when modeling laboratory data. The idea is to extend these architectures to handle real-world sensory input as that input continuously unfolds through time.
Some of the fundamental questions to still be answered in cognitive robotics are:
- How much human programming should or can be involved to support the learning processes?
- How can one quantify progress? Some of the adopted ways is the reward and punishment. But what kind of reward and what kind of punishment? In humans, when teaching a child for example, the reward would be candy or some encouragement, and the punishment can take many forms. But what is an effective way with robots?
- Intelligent agent
- Cognitive science
- Developmental robotics
- Embodied cognitive science
- Epigenetic robotics
- Evolutionary robotics
- Hybrid intelligent system
- Intelligent control
- The Symbolic and Subsymbolic Robotic Intelligence Control System (SS-RICS)
- Intelligent Systems Group - University of Utrecht
- The Cognitive Robotics Group - University of Toronto
- Cognitive Robotics Lab of Juergen Schmidhuber at IDSIA and Technical University of Munich
- What Does the Future Hold for Cognitive Robots? - Idaho National Laboratory
- Cognitive Robotics at the Naval Research Laboratory
- Cognitive robotics at ENSTA autonomous embodied systems, evolving in complex and non-constraint environments, using mainly vision as sensor.
- The Center for Intelligent Systems - Vanderbilt University
- CoR-Lab AT Bielefeld University
- SocioCognitive Robotics at Delft University of Technology
- Autonomous Systems Laboratory at Universidad Politecnica de Madrid
- iRobis Announces Complete Cognitive Software System for Robots
- The Xpero project
- Institute of Robotics in Scandinavia AB (iRobis)
- RoboBusiness: Robots that Dream of Being Better