Research On Behavior Learning Methods For Intelligent Robot With Cognitive Ability

Behavior learning is one of the key techniques for intelligent robot design. Nowadays, behavior learning methods of robot is limited to reflex behavior learning. Knowledge representation structure of tasks is given by human beforehand, and training samples is used for parameter tuning. Once the task is changed, reprogramming is needed. Systems that possess such behavior learning capability do not have the cognitive ability, and are unable to emerge complex intelligent behavior. Research on the robotics systems with cognitive ability is becoming an important research direction of robotics, which is closely related to cognitive psychology, cognitive science and animal behavior.This thesis focuses on the research of the cognitive mechanism of robotics, and thoroughly analyses the importance of cognitive model to the development of robot's intelligence. The architecture of intelligent robots with cognitive ability is presented, knowledge representation and learning methods of the cognitive model is thoroughly studied. Finally, the results are used to achieve environmental spatial cognition, and emerge the multi-tasks'planning behavior in a bottom-up way. The main contributions are as follows:Firstly, the paradigm of robot architecture is reclassificated from the viewpoint of intelligence acquisition. New paradigm classification not only covers the traditional paradigm, but also completes the cognitive levels of intelligent robot, differentiates the intelligent levels of robot systems, and specifies the importance of cognitive ability in the paradigm of robot architecture. Based on this, this thesis presents the architecture of intelligent robots with cognitive ability, which realizes autonomous learning, only needs the fundamental reflex behavior, and acquires the high-level cognitive ability through autonomous learning, instead of reprogramming. Modules are dependant on each other, learning synchronously, and so, possess the ability of real-time learning. Secondly, self-organized extraction process of the environmental features is studied."Active exploration behavior"and"sensory-motor coordination"are used to acquire environmental features. Design method of the activity neurons based on variaty detection and activation intensity is presented. The growing dynamic self-organizing feature map (GDSOM) is presented to extract and recognize the landmark. Experiment results show that this landmark extracting method does not need exact location control and sensor metric model, while, possesses better robustness and less computing burden, which effectively solves the problem of"perception variability", and builds the foundation for cognitive ability.Thirdly, the knowledge representation and learning method of spatio-temporal experience are studied. The cognitive mathematical model, that is observation-drived Markov decision process (ODMDP), is discussed, and the solving strategy is proposed. Referring the characteristics of biological neuron, a new biological neural network model, spatio-temporal associative memory networks (STAMN), is proposed to realize the incremental learning of state and action. The state localization problem of ODMDP is resolved. STAMN proposed here is applied to achieve environmental spatial cognition. Experiment results show that this network can effectively solve the SLAM problem for large-scale circular environment.Finally, the reinforcement learning methods with cognitive ability are studied. Reinforcement learning model, which resolves strategy learning problem of ODMDP, and a (k-M)(k-P) Sarsa algorithm are proposed for the multi-tasks learning problem of robot. Their feasibility and effectiveness are validated by the maze environment multi-tasks experiments.