Spec: Course Level 11 //  Semester: 2  // Time & Place: Mon 10am  & Thu 10am ( JCMB 3317)

[Announcements] [Syllabus] [Project teams] [Homeworks] [Grading] [Class Schedule/Lecture Notes]
Dr. Sethu Vijayakumar

2107F, JCMB, The King's Buildings,
School of Informatics, Univ. of Edinburgh
Announcements (Spring 2007)
  • 08-03-2007: HW2 papers have been allocated.
Control of complex, compliant, multi degree of freedom (DOF) sensorimotor systems like humanoid robots or autonomous vehicles have been pushing the limits of traditional control theoretic methods. This course aims at introducing adaptive and learning control as a viable alternative. The course 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 sensorimotor control. Issues and possible approaches for multimodal sensor integration, sensorimotor transformations and learning in high dimensions 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

The syllabus covered in this course falls under the following three broad categories: (a) Basics of control theory and sensorimotor learning, (b) Machine learning techniques geared towards learning and control in high dimensions and (c) Fundamentals of human and biological motor control from a computational modeling perspective.

Fundamental Control Theory
Classical Control: PD, PID
Model based vs Direct control
Limitations of traditional control (Why learning control?)

Machine Learning Tools for Learning Control
Intrinsic Dimensionality & Dimensionality Reduction
Multiple Model Learning
Synergistic Control and Activation
Coordinate Transformations: Body centric, Retinotopic, Object Centered

Adaptive and Learning Control
Trajectory Planning, Inverse Kinematics and Inverse Dynamics
Nonparametric methods for learning
Real time and online learning
Distal Learning Problem

Predictive Control
Kalman filters, Extended Kalman Filters
Particle Filters
Movement Primitives
Dynamical Systems as Movement Policies
Extracting, Tuning and Learning Movement Primitives
Rhythmic vs Point-to-point primitives

Biological & Human Motor Control
Force Field Hypothesis, Equilibrium Point Hypothesis
Internal Models
Tuning Curves and Force Adaptation

Sensorimotor Integration
Bayesian Cue Integration
Multimodal sensor fusion
Explaining away and Cue reliability based integration

Class Schedule & Lecture Notes (Check out this page)

Class Format
The course consists of lectures with discussions, reading assignments, and homework assignments. One of these assignments might involve a group mini-project. There will be a final exam covering the basics of the course.

Textbook & Reading Materials
This course has no single prescribed textbook. The following are suggested readings. We will rely on class notes and slides which will be distributed in class. All examinable material will be covered in class.

  • Applied Non-linear Control (Slotine and Li, 1991, Prentice Hall)
  • The Elements of Statistical Learning (Hastie, Tibshirani & Friedman, 2001, Springer Verlag)
  • Other Suggested Reading
    • Control of Human movement (Latash, M. 1993, Human Kinetics Pub.)
    • Current Opinions in Neurobiology, Special issue on Motor Control, Vol. 9, Issue 6, 1999, Elsevier Science.
    • Current Opinions in Neurobiology, Vol. 10, Issue 6, 2000, Elsevier Science.
    • Current Opinions in Neurobiology, Vol. 11, Issue 6, 2001, Elsevier Science.
    • Generalized Additive Models (Hastie, Tibshirani, 1995, Chapman & Hall)
  • 2 Homework Assignments: 35% (incl. programming project)
  • 1 paper presentation (+ lit. survey): 15%
  • 1 Final Exam: 50%

Introduction to Vision and Robotics (IVR) is a pre-requisite for this course. LFD is a recommended pre-requisite. In addition, the students should have a good grounding in mathematics and be comfortable with linear algebra and matrix computations. A basic understanding of control theory is desirable.

Academic Integrity
All students are required to abide by the regulations and guidelines laid down by the University of Edinburgh. Various documents relating to the guidelines can be viewed at the UoE regulations & guidelines page for students. If you have any questions about the responsibilities of either students, faculty, or graders under this policy, contact the instructor or the ITO.

If you have comments or suggestions, send email to sethu.vijayakumar[at]