Obrigado por enviar sua consulta! Um dos membros da nossa equipe entrará em contato com você em breve.
Obrigado por enviar sua reserva! Um dos membros da nossa equipe entrará em contato com você em breve.
Programa do Curso
Introduction to Robot Learning
- Overview of machine learning in robotics
- Supervised vs unsupervised vs reinforcement learning
- Applications of RL in control, navigation, and manipulation
Fundamentals of Reinforcement Learning
- Markov decision processes (MDP)
- Policy, value, and reward functions
- Exploration vs exploitation trade-offs
Classical RL Algorithms
- Q-learning and SARSA
- Monte Carlo and temporal difference methods
- Value iteration and policy iteration
Deep Reinforcement Learning Techniques
- Combining deep learning with RL (Deep Q-Networks)
- Policy gradient methods
- Advanced algorithms: A3C, DDPG, and PPO
Simulation Environments for Robot Learning
- Using OpenAI Gym and ROS 2 for simulation
- Building custom environments for robotic tasks
- Evaluating performance and training stability
Applying RL to Robotics
- Learning control and motion policies
- Reinforcement learning for robotic manipulation
- Multi-agent reinforcement learning in swarm robotics
Optimization, Deployment, and Real-World Integration
- Hyperparameter tuning and reward shaping
- Transferring learned policies from simulation to reality (Sim2Real)
- Deploying trained models on robotic hardware
Summary and Next Steps
Requisitos
- An understanding of machine learning concepts
- Experience with Python programming
- Familiarity with robotics and control systems
Audience
- Machine learning engineers
- Robotics researchers
- Developers building intelligent robotic systems
21 Horas
Declaração de Clientes (1)
o seu conhecimento e a utilização da IA para Robotics no futuro.
Ryle - PHILIPPINE MILITARY ACADEMY
Curso - Artificial Intelligence (AI) for Robotics
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