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Robot Learning & Sensorimotor Control (RLSC)

Course Description

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 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.
This MSc course (designed as a follow up to the introductory course on robotics (R:SS) in Semester 1) will gear students towards specialized topics in robot control and planning as well as human motor control from a machine learning perspective and is a must for students looking to pursue a post-graduate degree in robotics or human motor control.

Level 11 SCQF Official Course Descriptor

When and Where?

Semester: 2  Time: 10:00-10:50 
Location and Days:
Monday and Thursday (David Hume Tower LG.11)
First Class: Jan 16, 2016 (Monday)


Prof. Sethu VIjayakumar

Teaching Assistant: Vladimir Ivan (


(Refer to the Lecture Notes tab on the left for the detailed schedule and resources - updated weekly)

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


One Homework Assignment - 30%
One Oral Presentation - 10%
Final Exam - 60%

Late Coursework & Extension Requests
Academic Misconduct

Background and Prerequisites

The student taking this course should ideally have had some prior exposure to robotics basics (such as R:SS) and/or machine learning basics.
The student should have some feel for the formulation and use of mathematical models and possess sufficient mathematical maturity in order to be able to follow some readings from the research literature. On the practical side, one of the assignments will require programming, in an environment such as MATLAB.

Suggested Reading

  • Chris Bishop, Pattern Recognition and Machine Learning [link to resources]
  • Hastie, Tibshirani and Friedman, The Elements of Statistical Learning, Springer-Verlag. [link to book pdf]
  • 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
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