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Course Outline
Foundations of Robotic Manipulation and Deep Learning
- Overview of manipulation tasks and system architecture
- Comparison of traditional versus learning-based methodologies
- Role of deep learning in perception, planning, and control
Perception for Manipulation
- Visual sensing and object detection tailored for grasping
- 3D vision, depth sensing, and point cloud analysis
- Training Convolutional Neural Networks (CNNs) for object localization and segmentation
Grasp Planning and Detection
- Traditional 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
- Configuration of ROS 2 nodes for perception and control
- Simulation of robotic manipulators using Gazebo and Isaac Sim
- Integration of neural models for real-time control
End-to-End Learning for Manipulation
- Unifying perception, policy, and control within single networks
- Leveraging demonstration data for supervised policy learning
- Domain adaptation between simulation and physical hardware
Evaluation and Optimization
- Metrics for assessing grasp success, stability, and precision
- Testing under diverse conditions and disturbances
- Model compression and deployment on edge devices
Practical Project: Robotic Grasping with Deep Learning
- Designing a perception-to-action pipeline
- Training and testing a grasp detection model
- Integrating the model into a simulated robotic arm
Requirements
- A solid grasp of robotic kinematics and dynamics
- Proficiency in Python and deep learning frameworks
- Knowledge of ROS or comparable robotic middleware
Target Audience
- Robotics engineers creating intelligent manipulation systems
- Perception and control experts specializing in 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.
