Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Biped Locomotion
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Control in Robotics and Automation: Sensor-Based Integration
Control in Robotics and Automation: Sensor-Based Integration
Dynamic balance of a biped robot using fuzzy reinforcement learning agents
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
Extracting fuzzy control rules from experimental human operatordata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Information combination operators for data fusion: a comparative review with classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A parametric model for fusing heterogeneous fuzzy data
IEEE Transactions on Fuzzy Systems
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How to learn from both sensory data (numerical) and a prior knowledge (linguistic) for a robot to acquire perception and motor skills is a challenging problem in the field of autonomous robotic systems. To make the most use of the information available for robot learning, linguistic and numerical heterogeneous dada (LNHD) integration is firstly investigated in the frame of the fuzzy data fusion theory. With neural systems' unique capabilities of dealing with both linguistic information and numerical data, the LNHD can be translated into an initial structure and parameters and then robots start from this configuration to further improve their behaviours. A neural-fuzzy-architecture-based reinforcement learning agent is finally constructed and verified using the simulation model of a physical biped robot. It shows that by incorporation of various kids of LNHD on human gait synthesis and walking evaluation the biped learning rate for gait synthesis can be tremendously improved.