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Duration 28 hours
Course Outline
Foundations of Robotic Manipulation and Deep Learning
- An overview of manipulation tasks and essential system components
- Comparing traditional methods with learning-based approaches
- The role of deep learning in perception, planning, and control
Perception Systems for Manipulation
- Visual sensing techniques and object detection for grasping
- 3D vision, depth sensing, and point cloud processing methods
- Training CNNs for accurate object localization and segmentation
Grasp Planning and Detection
- Review of classical grasp planning algorithms
- Learning grasp poses through data and simulation
- Implementation of grasp detection networks (e.g., GGCNN, Dex-Net)
Control and Motion Planning
- Inverse kinematics and trajectory generation techniques
- Learning-based motion planning and imitation learning strategies
- Reinforcement learning for developing manipulation control policies
Integration with ROS 2 and Simulation Platforms
- Configuring ROS 2 nodes for perception and control tasks
- Simulating robotic manipulators in Gazebo and Isaac Sim
- Integrating neural models to achieve real-time control
End-to-End Learning for Manipulation
- Unifying perception, policy, and control within comprehensive networks
- Leveraging demonstration data for supervised policy learning
- Applying domain adaptation between simulation and physical hardware
Evaluation and Optimization
- Key metrics for assessing grasp success, stability, and precision
- Testing performance under diverse conditions and disturbances
- Model compression and deployment strategies for edge devices
Practical Project: Deep Learning-Driven Robotic Grasping
- Designing a complete perception-to-action pipeline
- Training and validating a grasp detection model
- Integrating the model into a simulated robotic arm setup
Requirements
- A strong grasp of robotics kinematics and dynamics
- Proficiency in Python and deep learning frameworks
- Experience with ROS or comparable robotic middleware
Intended Audience
- Robotics engineers developing intelligent manipulation systems
- Perception and control specialists focused 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.
