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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Robot Learning and Sensorimotor Control (INFR11091)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryThis course is designed as a follow up to the introductory course on Robotics: Science and Systems and will gear students towards advanced topics in robot control and planning from a machine learning perspective.
Control of complex, compliant, multi degree of freedom (DOF) sensorimotor systems like humanoid robots or autonomous vehicles have been pushing the limits of traditional planning and control methods.
This course aims at introducing a machine learning approach to the challenges and will take the students through various aspects involved in motor planning, control, estimation, prediction and learning with an emphasis on the computational perspective. We will learn about statistical machine learning tools and methodologies particularly geared towards problems of real-time, online learning for robot control.
Issues and possible approaches for learning in high dimensions, planning under uncertainty and redundancy, sensorimotor transformations and stochastic optimal control will be discussed. This will be put in context through exposure to topics in human motor control, experimental paradigms and the use of computational methods in understanding biological sensorimotor mechanisms.
Course description The syllabus has will cover Machine Learning concepts relevant for Robotics, Adaptive and Learning Control, Planning and basics of Human Sensorimotor Control.

Machine Learning Tools for Robotics
- Regression in High Dimensions
- Dimensionality Reduction
- Online, incremental learning
- Multiple Model Learning

Adaptive Learning and Control

Predictive Control

Movement Primitives
- Rhythmic vs Point to Point Movements
- Dynamical Systems and DMPs

Planning and Optimization
- Stochastic Optimal Control (2)
- Bayesian Inference Planning
- RL, Apprenticeship Learning and Inverse Optimal Control

Understanding Human Sensorimotor Control
- Force Field and Adaptation
- Optimal control theory for Explaining Sensorimotor Behaviour
- Cue Integration and Sensorimotor Adaptation
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements This course is open to all Informatics students including those on joint degrees. For external students where this course is not listed in your DPT, please seek special permission from the course organiser.

Some prior exposure to robotics basics (such as Robotics: Science and Systems) and/or machine learning basics is recommended. A mathematical background is assumed. Programming will be required in an environment such as MATLAB.
Information for Visiting Students
High Demand Course? Yes
Course Delivery Information
Academic year 2015/16, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 18, Summative Assessment Hours 2, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 78 )
Assessment (Further Info) Written Exam 60 %, Coursework 40 %, Practical Exam 0 %
Additional Information (Assessment) You should expect to spend approximately 24 hours on the coursework for this course.

If delivered in semester 1, this course will have an option for semester 1 only visiting undergraduate students, providing assessment prior to the end of the calendar year.
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Demonstrate knowledge of key areas of robot dynamics control and kinematic planning.
  2. Analyze and evaluate conceptual and empirical problems in adaptive control and robot learning.
  3. Analyze and implement a subset of established learning algorithms in dynamics learning and stochastic optimal control.
  4. Demonstrate understanding of issues related to optimality in human motor control; develop ability to frame human motor control problems in an optimization framework.
Reading List
Howie Choset, Kevin M Lynch, Seth Hutchinson and George Kantor, Principles of Robot Motion: Theory, Algorithms, and Implementations

Mark W. Spong, Seth Hutchinson and M. Vidyasagar, Robot Modeling and Control

Sebastian Thrun, Wolfram Burgard and Dieter Fox, Probabilistic Robotics

Sciliano, Khatib (ed.) Springer Handbook of Robotics
Additional Information
Course URL
Graduate Attributes and Skills Not entered
KeywordsNot entered
Course organiserProf Sethu Vijayakumar
Tel: (0131 6)51 3444
Course secretaryMiss Maree Matheson
Tel: (0131 6)50 9989
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