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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2012/2013
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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
Course typeStandard AvailabilityAvailable to all students
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) Credits10
Home subject areaInformatics Other subject areaNone
Course website http://www.inf.ed.ac.uk/teaching/courses/rlsc/ Taught in Gaelic?No
Course descriptionThis course is designed as a follow up to the introductory course on robotics (R:SS) 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.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Additional Costs None
Information for Visiting Students
Pre-requisitesNone
Displayed in Visiting Students Prospectus?Yes
Course Delivery Information
Delivery period: 2012/13 Semester 2, Available to all students (SV1) Learn enabled:  No Quota:  None
Location Activity Description Weeks Monday Tuesday Wednesday Thursday Friday
CentralLecture1-11 10:00 - 10:50
CentralLecture1-11 10:00 - 10:50
First Class Week 1, Monday, 10:00 - 10:50, Zone: Central. Lecture Theatre 3, 7 Bristo Square
Exam Information
Exam Diet Paper Name Hours:Minutes
Main Exam Diet S2 (April/May)2:00
Summary of Intended Learning Outcomes
- demonstrate knowledge of key areas of robot dynamics control and kinematic planning.
- analyze and evaluate conceptual and empirical problems in adaptive control and robot learning.
- analyze and implement a subset of established learning algorithms in dynamics learning and stochastic optimal control;
- demonstrate understanding of issues related to optimality in human motor control; develop ability to frame human motor control problems in an optimization framework.
Assessment Information
Written Examination 60
Assessed Assignments 30
Oral Presentations 10
Special Arrangements
None
Additional Information
Academic description Not entered
Syllabus 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
Transferable skills Not entered
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
Study Abroad Not entered
Study Pattern Lectures: 18
Tutorials: 0
Timetabled Laboratories: 0
Non-timetabled assessed assignments: 24
Private Study/Other: 58
KeywordsNot entered
Contacts
Course organiserDr Michael Rovatsos
Tel: (0131 6)51 3263
Email: mrovatso@inf.ed.ac.uk
Course secretaryMiss Kate Weston
Tel: (0131 6)50 2701
Email: Kate.Weston@ed.ac.uk
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