自适应运动控制
PORAMATE MANOONPONG
目录
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1 Embodied AI: Design Principles of Intelligence
1.1 Introduction to Embodied AI
1.2 Properties of Embodied Agents
1.3 8 Design Principles for Embodied Agents Toward Embodied AI
2 Locomotion Principles in Animals
2.1 Biomechanics of Animals
2.2 Biological Neural Mechanisms for Animal Locomotion
2.3 Terminology for Locomotion
3 Biomechanics and Locomotion Control for Walking Robots
3.1 Biomechanics of Robots
3.2 Robot sensors for locomotion control
3.3 Locomotion control of walking robots
4 Neural Locomotion Control I
4.1 Central Pattern Generators (CPGs)
4.2 Frequency Adaptation for Locomotion Control
4.3 CPG implementation on robot simulation for locomotion control
5 Neural Locomotion Control II
5.1 Premotor Neural Networks for Directional and Gait Control
5.2 Velocity Regulating Network
5.3 Phase Switch Network
5.4 VRN implementation on robot simulation for directional control
6 Neural Sensory Preprocessing
6.1 Neurodynamics
6.2 Sensor-driven Behavior Control
6.3 Neural preprocessing implementation on robot simulation for autonomous obstacle avoidance
6.4 Recurrent Neural Networks
7 Learning and Adaptation for Adaptive Locomotion I
7.1 Correlation based Learning
7.2 Error-based Learning
7.3 Goal-direct behavior control
7.4 Robot learning implementation on simulation for adaptive behavior
8 Learning and Adaptation for Adaptive Locomotion II
8.1 Hormone for Robot Adaptation
8.2 Multiple CPGs with Force Feedback for Self-Organized Locomotion
8.3 Bio-inspired Neural Control Applications
Introduction to Embodied AI
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