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Duration 28 hours
Course Outline
Introduction to Robotic Manipulation and Deep Learning
- Survey of manipulation tasks and constituent system elements
- Contrast between traditional and learning-based methodologies
- Application of deep learning in perception, planning, and control
Perception for Manipulation
- Visual sensing and object detection for grasping purposes
- 3D vision, depth sensing, and point cloud analysis
- Training CNNs for object localization and segmentation
Grasp Planning and Detection
- Conventional grasp planning algorithms
- Learning grasp poses from data and simulation environments
- Implementation of grasp detection networks (e.g., GGCNN, Dex-Net)
Control and Motion Planning
- Inverse kinematics and trajectory creation
- 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
- Simulating robotic manipulators in Gazebo and Isaac Sim
- Incorporating neural models for real-time control
End-to-End Learning for Manipulation
- Unifying perception, policy, and control within integrated networks
- Utilizing demonstration data for supervised policy learning
- Domain adaptation between simulation and physical hardware
Evaluation and Optimization
- Metrics for grasp success, stability, and accuracy
- Testing under diverse conditions and disturbances
- Model compression and deployment on edge devices
Hands-on Project: Deep Learning-Based Robotic Grasping
- Architecting a perception-to-action pipeline
- Training and evaluating a grasp detection model
- Incorporating the model into a simulated robotic arm
Requirements
- Solid command of robotics kinematics and dynamics
- Proficiency in Python and major deep learning frameworks
- Knowledge of ROS or comparable robotic middleware
Target Audience
- Robotics engineers engineering intelligent manipulation systems
- Perception and control specialists focused on grasping applications
- Researchers and senior practitioners specializing in robot learning and AI-driven 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.
