Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Robotic Manipulation and Deep Learning
- Overview of manipulation tasks and their core system components
- Comparison between traditional and learning-based approaches
- The role of deep learning in perception, planning, and control
Perception for Manipulation
- Visual sensing and object detection techniques for grasping
- 3D vision, depth sensing, and point cloud processing
- Training CNNs for object localization and segmentation
Grasp Planning and Detection
- Classical algorithms for grasp planning
- Learning grasp poses through data and simulation
- Implementing grasp detection networks such as GGCNN and Dex-Net
Control and Motion Planning
- Inverse kinematics and trajectory generation
- Learning-based motion planning and imitation learning
- Applying reinforcement learning to manipulation control policies
Integration with ROS 2 and Simulation Environments
- 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
- Unifying perception, policy, and control within single networks
- Utilizing demonstration data for supervised policy learning
- Domain adaptation between simulation and real hardware
Evaluation and Optimization
- Defining metrics for grasp success, stability, and precision
- Testing performance under varying conditions and disturbances
- Model compression and deployment on edge devices
Hands-on Project: Deep Learning-Based Robotic Grasping
- Designing a complete perception-to-action pipeline
- Training and evaluating a grasp detection model
- Integrating the model into a simulated robotic arm
Requirements
- A robust understanding of robotics kinematics and dynamics
- Proficiency in Python and deep learning frameworks
- Familiarity with ROS or comparable robotic middleware
Target Audience
- Robotics engineers focused on developing intelligent manipulation systems
- Perception and control specialists working on grasping applications
- Researchers and advanced practitioners in the fields of 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.
