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Dziękujemy za wysłanie rezerwacji! Jeden z członków naszego zespołu skontaktuje się z Państwem wkrótce.
Plan Szkolenia
Introduction to Robotic Manipulation and Deep Learning
- Overview of manipulation tasks and system components
- Traditional vs. learning-based approaches
- Deep learning in perception, planning, and control
Perception for Manipulation
- Visual sensing and object detection for grasping
- 3D vision, depth sensing, and point cloud processing
- Training CNNs for object localization and segmentation
Grasp Planning and Detection
- Classical grasp planning algorithms
- Learning grasp poses from data and simulation
- Implementing grasp detection networks (e.g., GGCNN, Dex-Net)
Control and Motion Planning
- Inverse kinematics and trajectory generation
- Learning-based motion planning and imitation learning
- Reinforcement learning for manipulation control policies
Integration with ROS 2 and Simulation Environments
- Setting up ROS 2 nodes for perception and control
- Simulating robotic manipulators in Gazebo and Isaac Sim
- Integrating neural models for real-time control
End-to-End Learning for Manipulation
- Combining perception, policy, and control in unified networks
- Using demonstration data for supervised policy learning
- Domain adaptation between simulation and real hardware
Evaluation and Optimization
- Metrics for grasp success, stability, and precision
- Testing under varying conditions and disturbances
- Model compression and deployment on edge devices
Hands-on Project: Deep Learning-Based Robotic Grasping
- Designing a perception-to-action pipeline
- Training and testing a grasp detection model
- Integrating the model into a simulated robotic arm
Summary and Next Steps
Wymagania
- Strong understanding of robotics kinematics and dynamics
- Experience with Python and deep learning frameworks
- Familiarity with ROS or similar robotic middleware
Audience
- Robotics engineers developing intelligent manipulation systems
- Perception and control specialists working on grasping applications
- Researchers and advanced practitioners in robot learning and AI-based control
28 godzin
Opinie uczestników (2)
Dobrze omówione przez trenera przykłady ćwiczeń
Mariusz - Politechnika Opolska
Szkolenie - Artificial Intelligence (AI) for Mechatronics
jego wiedzy i wykorzystania sztucznej inteligencji dla Robotics w przyszłości.
Ryle - PHILIPPINE MILITARY ACADEMY
Szkolenie - Artificial Intelligence (AI) for Robotics
Przetłumaczone przez sztuczną inteligencję
