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Duration 28 hours
Course Outline
Introduction to Robotic Manipulation and Deep Learning
- Summary of operational tasks and critical system components
- Comparative analysis of conventional methods versus learning-based frameworks
- Integration of deep learning in perception, strategic planning, and control mechanisms
Perception for Manipulation
- Visual sensing protocols and object detection for grasping operations
- Three-dimensional vision, depth sensing, and point cloud data processing
- Training Convolutional Neural Networks for precise object localization and segmentation
Grasp Planning and Detection
- Review of classical grasp planning algorithms
- Acquisition of grasp poses through data-driven methods and simulation
- Implementation of grasp detection networks (e.g., GGCNN, Dex-Net)
Control and Motion Planning
- Inverse kinematics solutions and trajectory generation standards
- Learning-based motion planning and imitation learning techniques
- Application of reinforcement learning for manipulation control policies
Integration with ROS 2 and Simulation Environments
- Configuration of ROS 2 nodes for perception and control functions
- Simulation of robotic manipulators in Gazebo and Isaac Sim environments
- Integration of neural models for real-time control applications
End-to-End Learning for Manipulation
- Synthesis of perception, policy, and control within unified network architectures
- Utilization of demonstration data for supervised policy learning
- Domain adaptation strategies between simulation and physical hardware
Evaluation and Optimization
- Establishment of metrics for grasp success, stability, and precision
- Performance testing under varying operational conditions and disturbances
- Model compression and deployment strategies for edge devices
Practical Project: Deep Learning-Based Robotic Grasping
- Design of a comprehensive perception-to-action pipeline
- Training and validation of a grasp detection model
- Integration of the model into a simulated robotic arm system
Requirements
- Comprehensive understanding of robotics kinematics and dynamics
- Proficiency in Python and deep learning frameworks
- Familiarity with ROS or similar robotic middleware platforms
Target 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
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.
