Cognitive Robotics

Uitgebreide vaknaam	Cognitive Robotics
Leerdoelen	After successful completion of this course, students will be able to: 1. Explain the main theories of open-ended learning and cognitive robotics. 2. Explain meaning of different concepts often used in the field of 3D computer vision and Human Robot Interaction and their application in robotics. 3. Exploit deep transfer learning algorithms for open-ended object category recognition. 4. Implement and experiment deep learning architectures for object grasping. 5. Create a tight coupling between object perception and manipulation and perform experiment using real Kinect data and a simulated Panda robotic arm. 6. Design a cognitive robotic system capable of dealing with unseen object categories and performing manipulation tasks in open-ended environments. 7. Use RACE cognitive robotic system, ROS, and Gazebo in practical projects. Remark: The course unit prepares students to do their graduation project if they choose to do it in robotics.
Omschrijving	Cognitive robots are expected to be more autonomous and efficiently work in human-centric environments. A cognitive robot should process very different types of information in varying time scales. Two different modes of processing, generally labeled as System1 and System2, are commonly accepted theories in cognitive psychology. The operations of System1 (i.e. perception and action) are typically fast, reactive, and intuitive. The operations of System2 (i.e. semantic) are slow, deliberative, and analytic. For such robots, open-ended learning for object perception and grasping is a challenging task due to the high demand for accurate and real-time responses in dynamic environments. In this course, "open-ended" implies that the set of object categories to be learned is not known in advance, and the training instances are extracted from online experiences of a robot, and become gradually available over time, rather than being completely available at the beginning of the learning process. This way the robot adapts its perception and grasping skills over time to different environments. This year's theme of the course is "Simultaneous Multi-View Object Grasping and Recognition in Open-Ended Domains". This course covers a diverse set of topics that focus on addressing the most critical aspects of building a cognitive robotic system. We recently wrote a survey paper about the state of lifelong learning in service robots (see https://link.springer.com/article/10.1007/s10846-021-01458-3). It covers all the topics of the cognitive robotics course in a concise and brief manner to help students in easy remembrance and quick revision. The course is a combination of lectures, reading sessions, and lab sessions. The lectures discuss the fundamentals of topics required to develop a cognitive robotic system. During the reading sessions, students present and discuss recent contributions in the fields of object perception and manipulation. Webpage of the course: https://rugcognitiverobotics.github.io/
Uren per week
Onderwijsvorm	Bijeenkomst (S), Hoorcollege (LC), Practisch werk (PRC) (Throughout the course, students will work partly individually and partly in groups of two or three on a related research project.)
Toetsvorm	Opdracht (AST), Practisch werk (PR), Verslag (R) (The grading policy for this course is based on an essay assignment (15%), practical works (35%), and a final project (50%). Students will need to score at least a 5.5 average and will have to get at least a 5.0 on each component. For the practical assignments, students will work partly independently and partly under supervision (mandatory attendance).)
Vaksoort	master
Coördinator	S.H. Mohades Kasaei, PhD.
Docent(en)	S.H. Mohades Kasaei, PhD.
Entreevoorwaarden	Mandatory: No prior knowledge is assumed. Please note that the student is expected to have a relevant BSc degree. Advised: Prior knowledge of Deep Learning and basic linear algebra would be useful but is not required. I will try to provide external study materials for those students who do not have sufficient knowledge of Deep Learning. For programming throughout the course, we mainly use C++/Python-based ROS Melodic. For your final project, you are free to choose MATLAB, Python, or C++ as your coding language.
Opmerkingen	This course unit has a capacity limit. More information about capacity-limit courses can be found here. This course has an intended limit of 75 participants. In case of COVID measures, we will provide remote access to the robotic lab's computers using AnyDesk software.
Opgenomen in	Opleiding Jaar Periode Type MSc Artificial Intelligence  (C - Elective Course Units) - semester I a keuze MSc Artificial Intelligence  (B - Mandatory Course Units Computational Intelligence) 1 semester I a verplicht CI MSc Artificial Intelligence  (B - Mandatory Course Units Robotics) 1 semester I a verplicht Robotics MSc Computational Cognitive Science  (C - Elective Course Units) - semester I a keuze