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Machine Learning & Robotics Lab


Computer Science Department


Robotics Course, WS 14/15, U Stuttgart

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See my general teaching page for previous versions of this lecture.

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Robotics is an ultimate test of our progress in Artificial Intelligence, Machine Learning and Control Theory research. However, while these research fields consider general but idealized problem formulations, robotics has to deal with the specifics our concrete 3-dimensional physical world and eventually integrate methods and hardware in autonomous systems. Therefore robotics is more than an application of the above fields and requires specific knowledge of how to generate montion, physically interact with the environment and perceive it.


The lecture will give an introduction to robotics in four chapters:

Scope
Kinematics & Dynamics
goal: orchestrate joint movements for desired movement in task spaces
(Kinematic map, Jacobian, optimality principle of inverse kinematics, singularities, configuration/operational/null space, multiple simultaneous tasks, special task variables, trajectory interpolation, motion profiles; 1D point mass, damping \& oscillation, PID, general dynamic systems, Newton-Euler, joint space control, reference trajectory following, optimal operational space control)
Planning and optimization
goal: planning around obstacles, optimizing trajectories
(Path finding vs.\ trajectory optimization, local vs.\ global, Dijkstra, Probabilistic Roadmaps, Rapidly Exploring Random Trees, differential constraints, metrics; trajectory optimization, general cost function, task variables, transition costs, gradient methods, 2nd order methods, Dynamic Programming)
Control Theory
theory on designing optimal controllers
(Topics in control theory, optimal control, HJB equation, infinite horizon case, Linear-Quadratic optimal control, Riccati equations (differential, algebraic, discrete-time), controllability, stability, eigenvalue analysis, Lyapunov function)
Mobile robots
goal: localize and map yourself; walk
(State estimation, Bayes filter, odometry, particle filter, Kalman filter, Bayes smoothing, SLAM, joint Bayes filter, EKF SLAM, particle SLAM, graph-based SLAM)
Prerequisites
As a prerequisite, student should have basic knowledge of linear algebra, probability theory and optimization.
Organization
  • This is the central website of the lecture. Link to slides, exercise sheets, announcements, etc will all be posted here.
  • See the 01-introduction slides for further information.
Schedule, slides & exercises
date topics slides exercises
(due on 'date'+1)
14.10. Introduction & Organization 01-introduction e01-basics
21.10. Kinematics 02-kinematics e02-geometry
28.10. Kinematics (cont'd) e03-kinematics
4.11. Dynamics 03-dynamics e04-kinematics2
11.11. Dynamics (cont'd) e05-dynamics
18.11. Path Planning 04-pathPlanning e06-dynamics
25.11. Path Optimization 05-pathOptimization e07-pathFinding
2.12. Probabilities 06-probabilities cancelled
9.12. cancelled
16.12. Mobile Robotics 07-mobileRobotics e08-probabilities
6.1. (holiday) no exercises on Jan 7th
13.1. Mobile Robotics (cont'd) e09-particleAndKalmanFilter
20.1. Reinforcement Learning (brief overview) 10-RL
27.1. Control Theory 08-controlTheory e10-RL
3.2. Control Theory (cont'd) e11-riccati
main.problem.cpp 		Extra Exercise for those that need extra points: exx-kalmanSLAM
10.2. Summary 14-Robotics-script
resources
online lectures: books: history: state-of-the-art (major conferences):