Cognitive robotics

Cognitive robotics

a robot is a robot device that is built from inanimate matter. Its behavior in response to the environment is deterministic, based on how the robot was designed. Cognition is the process of acquiring and using knowledge about the world for goal-oriented purposes, such as survival. Cognitive robotics is then the branch of robotics that is concerned with endowing the robot with intelligent behavior by providing the robot 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. 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.

A cognitive robot should exhibit:

  • informational attitudes such as knowledge and beliefs
  • motivational attitudes such as preferences and goals
  • cognitive capabilities such as revising mental attitudes, reasoning, decision making, planning, as well as observing and communicating
  • physical capabilities to move in the physical world, and to interact safely with objects in that world, including manipulation of these objects

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.

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 little infant for example, the reward would be a chocolate or some encouragement, and the punishment will have many ways. But what is the effective way with robots?

See also


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