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Course Outline
Foundations of Robotic Manipulation and Deep Learning
- Review of manipulation tasks and system architectures
- Comparison between traditional and learning-based methods
- Application of deep learning in perception, planning, and control
Perception Techniques for Manipulation
- Visual sensing and object detection for grasping purposes
- 3D vision, depth sensing, and point cloud handling
- Training CNNs for object localization and segmentation
Grasp Planning and Identification
- Traditional grasp planning algorithms
- Learning grasp poses through data and simulation
- Implementing grasp detection networks (e.g., GGCNN, Dex-Net)
Control Systems 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 Platforms
- Configuring 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 into unified networks
- Utilizing demonstration data for supervised policy learning
- Adapting domains between simulation and physical hardware
Assessment and Optimization
- Metrics for grasping success, stability, and accuracy
- Testing under varying conditions and disturbances
- Model compression and deployment on edge devices
Practical Project: Deep Learning-Driven Robotic Grasping
- Designing a perception-to-action pipeline
- Training and evaluating a grasp detection model
- Integrating the model into a simulated robotic arm
Requirements
- Robust knowledge of robotic kinematics and dynamics
- Proficiency with Python and deep learning frameworks
- Knowledge of ROS or comparable robotic middleware
Target Audience
- Robotics engineers designing intelligent manipulation systems
- Specialists in perception and control focused on grasping applications
- Researchers and advanced practitioners specializing in robot learning and AI-driven control
28 Hours
Testimonials (2)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.
Arjan Bakema
Course - Autonomous Navigation & SLAM with ROS 2
its knowledge and utilization of AI for Robotics in the Future.
