Postgraduate Course: Robot Learning and Sensorimotor Control (INFR11091)
Course Outline
School | School of Informatics |
College | College of Science and Engineering |
Course type | Standard |
Availability | Available to all students |
Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
Home subject area | Informatics |
Other subject area | None |
Course website |
http://www.inf.ed.ac.uk/teaching/courses/rlsc/ |
Taught in Gaelic? | No |
Course description | This 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 |
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Co-requisites | |
Prohibited Combinations | |
Other requirements | None |
Additional Costs | None |
Information for Visiting Students
Pre-requisites | None |
Displayed in Visiting Students Prospectus? | Yes |
Course Delivery Information
Not being delivered |
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 |
Keywords | Not entered |
Contacts
Course organiser | Dr Michael Rovatsos
Tel: (0131 6)51 3263
Email: |
Course secretary | Miss Kate Weston
Tel: (0131 6)50 2701
Email: |
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