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Course Outline
Foundations of Robotic Manipulation and Deep Learning
- Overview of manipulation tasks and core system components
- Comparison of traditional versus learning-based methodologies
- Applications of deep learning in perception, planning, and control
Perception Systems for Manipulation
- Visual sensing and object detection techniques for grasping
- 3D vision, depth sensing, and point cloud processing workflows
- Training CNNs for accurate object localization and segmentation
Grasp Planning and Detection Strategies
- Review of classical grasp planning algorithms
- Learning grasp poses through data-driven approaches and simulation
- Implementing grasp detection networks such as GGCNN and Dex-Net
Control Systems and Motion Planning
- Inverse kinematics and trajectory generation principles
- Learning-based motion planning and imitation learning techniques
- Reinforcement learning for developing manipulation control policies
Integration with ROS 2 and Simulation Platforms
- Configuring ROS 2 nodes for perception and control functions
- Simulating robotic manipulators using Gazebo and Isaac Sim
- Integrating neural models for real-time control operations
End-to-End Learning for Manipulation Tasks
- Synthesizing perception, policy, and control within unified networks
- Leveraging demonstration data for supervised policy learning
- Managing domain adaptation between simulation and physical hardware
Evaluation and Optimization Processes
- Defining metrics for grasp success, stability, and precision
- Testing performance under varying conditions and disturbances
- Model compression and deployment strategies for edge devices
Practical Project: Deep Learning-Driven Robotic Grasping
- Architecting a perception-to-action pipeline
- Training and validating a grasp detection model
- Integrating the model into a simulated robotic arm setup
Requirements
- A robust understanding of robotics kinematics and dynamics.
- Practical experience with Python and deep learning frameworks.
- Proficiency with ROS or comparable robotic middleware.
Intended Audience
- Robotics engineers developing intelligent manipulation systems.
- Specialists in perception and control focusing on grasping applications.
- Researchers and senior 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.
