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