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Course Outline
Introduction to Robotic Manipulation and Deep Learning
- Overview of manipulation tasks and essential system components
- Comparison of traditional methods 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
- Advanced 3D vision, depth sensing, and point cloud processing
- Training Convolutional Neural Networks (CNNs) for object localization and segmentation
Grasp Planning and Detection
- Review of classical grasp planning algorithms
- Learning grasp poses through data analysis 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
- Application of reinforcement learning for manipulation control policies
Integration with ROS 2 and Simulation Environments
- 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
- Synthesizing perception, policy, and control within unified networks
- Leveraging demonstration data for supervised policy learning
- Addressing 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 strategies for edge devices
Hands-on Project: Deep Learning-Based Robotic Grasping
- Designing a complete perception-to-action pipeline
- Training and validating 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 experience with deep learning frameworks
- Familiarity with ROS or comparable robotic middleware
Target Audience
- Robotics engineers designing intelligent manipulation systems
- Specialists in perception and control focused on grasping applications
- Researchers and advanced practitioners specializing 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.
