Teaching
This project aimed to generate behavior trees for autonomous underwater robotics missions. A Large Language Model (LLM) was used to verify the feasibility of the mission based on human input and the robot's abilities. The behavior trees were first executed in the simulator (StoneFish) and, if feasible, in real-world environments.
This project focused on solving the problem of waste collection in a maritime setting. The collection was performed by a robotic arm mounted on an Autonomous Surface Vehicle (ASV), also known as BlueBoat. The waste was localized using a vision system, which also supported navigation and precise task execution.
Understand the concept of the Behavior Trees framework. Learn how to use the Behavior Trees framework in practice and how to apply them with ROS2.
The course will give you the state-of-the-art opportunity to be familiar with the general concept of reinforcement learning and to deploy theory into practice by running coding exercises and simulations in ROS.
In this course, you are going to learn about machine learning concepts applicable to robotics; understand the fundamental principles of artificial intelligence and how the robots think and make independent decisions; make robots smarter and more collaborative; and advance your skills in Python programming.
My prestentaion of ROS 2 to Yaskawa software and application engineers.
My lecture for Robotics Systems Science course.
My presentation providing genrral overwie about the concept and software stackup used in most common open-source projects.
