Acquiring Data for Learning: Modern Approaches and Challenges

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Related lectures (31)

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Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Transformers in Robotics: Applications and Innovations

Covers the use of transformers in robotics, focusing on embodied perception and innovative applications in humanoid locomotion and reinforcement learning.

Learning and Adaptive Control for Robots: Extensions to DS

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Perceptual Robotics: Integrating Vision and Action

Covers the integration of visual perception and robotic actions in embodied AI.

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Delves into the definition and capabilities of robots based on various sources.

Neural Networks Optimization

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Robots: Safe Collaboration

Covers challenges and solutions for robots to work safely with humans, emphasizing adaptability and predictability.

Introduction to Reinforcement Learning: Concepts and Applications

Introduces reinforcement learning, covering its concepts, applications, and key algorithms.

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Explores learning and adaptive control for robots, focusing on challenges, path planning with dynamical systems, and real-time planning applications.

Reinforcement Learning: Policy Gradient and Actor-Critic Methods

Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.

Learning and Adaptive Control for Robots

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