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Course Outline
Introduction to Robotic Manipulation and Deep Learning
- Overview of manipulation tasks and system components.
- Comparison of 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 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.
- Utilizing reinforcement learning for manipulation control policies.
Integration with ROS 2 and Simulation Environments
- Setting up 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
- Combining perception, policy, and control within unified networks.
- Using demonstration data for supervised policy learning.
- Addressing domain adaptation between simulation and real hardware.
Evaluation and Optimization
- Metrics for 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.
Summary and Next Steps
Requirements
- A strong understanding of robotics kinematics and dynamics.
- Practical experience with Python and deep learning frameworks.
- Familiarity with ROS or comparable robotic middleware.
Audience
- Robotics engineers developing intelligent manipulation systems.
- Specialists in perception and control working on grasping applications.
- Researchers and advanced practitioners in robot learning and AI-driven 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.
