Merci d'avoir envoyé votre demande ! Un membre de notre équipe vous contactera sous peu.
Merci d'avoir envoyé votre réservation ! Un membre de notre équipe vous contactera sous peu.
Plan du cours
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
Pré requis
- 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 Heures
Nos clients témoignent (2)
Fourniture des matériels (machine virtuelle) pour passer directement aux exercices, et explication des fondamentaux de ROS2. Comprendre pourquoi les choses fonctionnent d'une manière particulière.
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ses connaissances et son utilisation de l'IA pour la robotique dans le futur.
Ryle - PHILIPPINE MILITARY ACADEMY
Formation - Artificial Intelligence (AI) for Robotics
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