Technical Note: \cal Q-Learning
Machine Learning
Neural control of rhythmic arm movements
Neural Networks - Special issue on neural control and robotics: biology and technology
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Fuzzy-logic zero-moment-point trajectory generation for reduced trunk motions of biped robots
Fuzzy Sets and Systems - Special issue: Fuzzy set techniques for intelligent robotic systems
The Human-size Humanoid Robot That Can Walk, Lie Down and Get Up
International Journal of Robotics Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Forces acting on a biped robot. Center of pressure-zero moment point
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This paper studies the parameters contained in the truncated Fourier series (TFS) formulation for bipedal walking balance control. Using the TFS generated lateral motion reference, 3D bipedal walking can be directly achieved without any parameter adjustment. Furthermore, the potential of this TFS formulation for motion balance control has also been investigated. One more motion balance strategy is developed through the reinforcement learning, which adjusts the motion's reference trajectory according to the selected dynamic feedback in real time. Dynamic simulation results of the presented balance control method show that the resulting motion can be constrained periodical and long-distance 3D bipedal walking motions are achievable.