Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Foundations of Robotic Manipulation and Deep Learning
- Overview of manipulation tasks and constituent system components
- Comparison of traditional methods versus learning-based approaches
- Application of deep learning in perception, planning, and control
Perception Strategies for Manipulation
- Visual sensing and object detection techniques for grasping
- 3D vision, depth sensing, and point cloud data processing
- Training CNNs for object localization and segmentation
Grasp Planning and Detection Mechanisms
- Review of classical grasp planning algorithms
- Acquiring grasp poses through data and simulation
- Implementation of grasp detection networks (e.g., GGCNN, 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
- Incorporating neural models for real-time control operations
End-to-End Learning for Robotic Manipulation
- Synthesizing perception, policy, and control within unified networks
- Leveraging demonstration data for supervised policy learning
- Domain adaptation techniques bridging simulation and real hardware
Evaluation and Performance Optimization
- Metrics for assessing grasp success, stability, and precision
- Testing performance under varying conditions and disturbances
- Model compression strategies and deployment on edge devices
Practical Project: Deep Learning-Driven Robotic Grasping
- Designing a complete perception-to-action pipeline
- Training and validating a grasp detection model
- Integrating the model into a simulated robotic arm environment
Requirements
- Solid grasp of robotics kinematics and dynamics
- Proficiency in Python and major deep learning frameworks
- Knowledge of ROS or comparable robotic middleware
Target Audience
- Robotics engineers designing intelligent manipulation systems
- Perception and control specialists focused on grasping applications
- Researchers and senior practitioners engaged 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.
