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.
Duration 28 hours (4 days)
Course Outline
Introduction to Robotic Manipulation and Deep Learning
- Overview of manipulation tasks and system components.
- Comparing traditional versus 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 grasp planning algorithms.
- Learning grasp poses from data and simulation.
- Implementing grasp detection networks (e.g., GGCNN, Dex-Net).
Control 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 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 in single networks.
- Leveraging demonstration data for supervised policy learning.
- Domain adaptation between simulation and real hardware.
Evaluation and Optimization
- Metrics for assessing grasp success, stability, and precision.
- Testing under varying conditions and disturbances.
- Model compression and deployment on edge devices.
Hands-on Project: Deep Learning-Based Robotic Grasping
- Designing a perception-to-action pipeline.
- Training and testing a grasp detection model.
- Integrating the model into a simulated robotic arm.
Requirements
- Proficiency in robotics kinematics and dynamics.
- Experience with Python and deep learning frameworks.
- Familiarity with ROS or similar robotic middleware.
Target Audience
- Robotics engineers building 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.
