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Course Outline
Foundations of Robotic Manipulation and Deep Learning
- Exploring manipulation tasks and core system components
- Comparing traditional methods with learning-based strategies
- The role of deep learning in perception, planning, and control
Perception Capabilities for Manipulation
- Visual sensing and object detection techniques for grasping
- 3D vision, depth sensing, and processing point clouds
- Training CNNs for accurate object localization and segmentation
Grasp Planning and Detection Strategies
- Reviewing classical grasp planning algorithms
- Learning grasp poses through data and simulation
- Implementing grasp detection networks (e.g., GGCNN, Dex-Net)
Control Systems and Motion Planning
- Mastering inverse kinematics and trajectory generation
- Applying learning-based motion planning and imitation learning
- Utilizing reinforcement learning for manipulation control policies
Integration with ROS 2 and Simulation Environments
- Configuring ROS 2 nodes for perception and control tasks
- Simulating robotic manipulators in Gazebo and Isaac Sim
- Integrating neural models for real-time control performance
End-to-End Learning for Manipulation Tasks
- Unifying perception, policy, and control in integrated networks
- Leveraging demonstration data for supervised policy learning
- Addressing domain adaptation between simulation and real hardware
Evaluation Metrics and Optimization Techniques
- Assessing grasp success, stability, and precision
- Testing robustness under varying conditions and disturbances
- Optimizing model compression for deployment on edge devices
Practical Project: Deep Learning-Based Robotic Grasping
- Designing a complete perception-to-action pipeline
- Training and validating a grasp detection model
- Integrating the model into a simulated robotic arm
Requirements
- Solid grasp of robotics kinematics and dynamics
- Proficiency in Python and deep learning frameworks
- Knowledge of ROS or comparable robotic middleware
Target Audience
- Robotics engineers building intelligent manipulation systems
- Perception and control experts focusing on grasping applications
- Researchers and senior practitioners in robot learning and AI-based 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.
