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Robot behaviour multi-level integrated learning method and robot behaviour multi-level integrated learning system

An integrated learning and robotics technology, applied in control/regulating systems, instruments, speed/acceleration control, etc., can solve the problems of lack of integration, long-time incentive learning and training, and inability to meet adaptive learning, so as to improve control capabilities Effect

Inactive Publication Date: 2013-04-17
CHONGQING UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, these learning systems have not been implemented on actual robots because of the long training time of incentive learning, or they only perform offline learning without integrating the important learning mode of "incentive learning".
Therefore, it cannot satisfy the adaptive learning of robot behavior in a dynamically changing environment.

Method used

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Embodiment Construction

[0040] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings; it should be understood that the preferred embodiments are only for illustrating the present invention, rather than limiting the protection scope of the present invention.

[0041] figure 1 A schematic diagram of the hierarchical learning system provided by the present invention; figure 2 It is a schematic diagram of the hierarchical learning structure of the three learning modes of the present invention, as shown in the figure: the robot behavior multi-level integrated learning method provided by the present invention includes the following steps:

[0042] S1: Input the robot-environment interaction perception information data and the state information data of the current movement action;

[0043] S2: According to the changes in the interactive perception information between the robot and the environment, obtain the environmental pattern...

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Abstract

The invention discloses a robot behaviour multi-level integrated learning method and a robot behaviour multi-level integrated learning system and relates to a robot behaviour control technology. The robot behaviour multi-level integrated learning system comprises a data sampling module, an unsupervised learning module, a supervised learning module, a motivation learning module and a command output module, wherein the data sampling module is used for inputting the information data of a robot; the unsupervised learning module acquires an environment mode characteristic vector which is used for representing the real-time change of an operating environment of the robot; the supervised learning module maps the environment mode characteristic vector into a motion command of a required behaviourof the robot in a real-time on-line mode by using the environment mode characteristic vector as an input signal; and the motivation learning module carries out real-time on-line optimization and accurate setting on parameters of a robot behaviour controller to make the controller operate reliably and stably. The method and the system provided by the invention can be applied to different types of robots which are capable of learning new robot behaviours and optimizing the conventional robot behaviours to adapt to the dynamic change of the operating environment, so that the intelligent and self-control capability of the robot is improved, the generality of the learning system is improved, and the design of the controller is simplified.

Description

technical field [0001] The invention relates to robot behavior control technology, in particular to a robot behavior learning system and method integrating multiple modes. Background technique [0002] There are already many engineering techniques for the design of robot behavior controllers, such as traditional AI based on symbolic reasoning, fuzzy logic, and mature linear control methods in cybernetics. However, these methods either require a definite environment model to realize "perception-plan-action", or require complex design and analysis of controller parameter tuning and system stability. Therefore, once the robot and its corresponding operating environment have any changes, the original planning strategy becomes outdated, and the designer has to redesign the controller, which increases the cost and cycle of system design. [0003] In recent years, there have also been some robot behavior learning systems based on neural networks that integrate more than two learni...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G05D13/04
Inventor 李军王斌任江洪黄毅卿
Owner CHONGQING UNIV
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