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Introduction to Robotics
Marc Toussaint
February 4, 2014
This is a direct concatenation and reformatting of all lecture slides and exercises from
the Robotics course (winter term 2013/14, U Stuttgart), including a topic list to prepare
for exams. Sorry for the sometimes imperfect formatting.
Contents
1 Introduction
3
2 Kinematics
6
Kinematic map, Jacobian, inverse kinematics as optimization problem, motion profiles, trajectory interpolation, multiple
simultaneous tasks, special task variables, configuration/operational/null space, singularities
3 Path Planning
15
Path finding vs. trajectory optimization, local vs. global, Dijkstra, Probabilistic Roadmaps, Rapidly Exploring Random
Trees, non-holonomic systems, car system equation, path-finding for non-holonomic systems, control-based sampling,
Dubins curves
4 Path Optimization
26
very briefly
5 Dynamics
27
1D point mass, damping & oscillation, PID, dynamics of mechanical systems, Euler-Lagrange equation, Newton-Euler
recursion, general robot dynamics, joint space control, reference trajectory following, operational space control
6 Probability Basics
33
7 Mobile Robotics
36
State estimation, Bayes filter, odometry, particle filter, Kalman filter, SLAM, joint Bayes filter, EKF SLAM, particle SLAM,
graph-based SLAM
8 Control Theory
44
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
51section.9
10 Reinforcement Learning in Robotics
55
11 SKIPPED THIS TERM – Grasping (brief intro)
61
Force closure, alternative/bio-inspired views
12 SKIPPED THIS TERM – Legged Locomotion (brief intro)
Why legs, Raibert hopper, statically stable walking, zero moment point, human walking, compass gait, passive walker
1
64

2
Introduction to Robotics, Marc Toussaint—February 4, 2014
13 Exercises
69
14 Topic list
79

Introduction to Robotics, Marc Toussaint—February 4, 2014
1
3
Introduction
Why Robotics?
1:1
Why Robotics?
• Commercial:
1:2
Industrial, health care, entertainment, agriculture, surgery, etc
• Critical view:
– International Committee for Robot Arms Control
– Noel Sharkey’s articles on robot ethics (Child care robots PePeRo..
Robotics as intelligence research
1:3
AI in the real world
AI: Machine Learning, probabilistic reasoning, optimization
Real World: Interaction, manipulation, perception, navigation,
etc
1:7
(robot “wife” aico)
1:4
Why AI needs to go real world

4
Introduction to Robotics, Marc Toussaint—February 4, 2014
Newton-Euler, joint space control, reference trajectory following, optimal operational space control
• Planning & 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 Pro-
Tunicates digest their brain once they settled!
• Motion was the driving force to develop intelligence
– motion needs control & decision making ↔ fast information
processing
– motion needs anticipation & planning
– motion needs perception
gramming
• Control Theory
theory on designing optimal controllers
Topics in control theory, optimal control, HJB equation, infinite horizon case, Linear-Quadratic
– motion needs spatial representations
optimal control, Riccati equations (differential, algebraic, discrete-time), controllability, stability,
• Manipulation requires to acknowledge the structure (geometry,
eigenvalue analysis, Lyapunov function
physics, objects) of the real world. Classical AI does not
1:8
• Mobile robots
goal: localize and map yourself
Robotics as intelligence research
• Machine Learning and AI are computational disciplines, which
had great success with statistical modelling, analysis of data
State estimation, Bayes filter, odometry, particle filter, Kalman filter, Bayes smoothing, SLAM,
joint Bayes filter, EKF SLAM, particle SLAM, graph-based SLAM
sets, symbolic reasoning. But they have not solved autonomous
1:11
learning, acting & reasoning in real worlds.
• Neurosciences and psychology are descriptive sciences, either
on the biological or cognitive level, e.g. with geat sucesses to
describe and cure certain deceases. But they are not sufficient
• Is this a practical or theoretical course?
to create intelligent systems.
• Robotics is the only synthetic discipline to understand intelli-
“There is nothing more practical than a good theory.”
(Vapnik, others...)
gent behavior in natural worlds. Robotic tells us what the actual
problems are when trying to organize behavior in natural worlds.
1:9
History
• Essentially, the whole course is about
reducing real-world problems to mathematical problems
that can be solved efficiently
1:12
•
Books
There is no reference book for this lecture. But a basic wellknown standard text book is:
1:10
Four chapters
• Kinematics & Dynamics
goal: orchestrate joint movements for desired movement in task
spaces
Craig, J.J.:
Introduction to
robotics: mechanics and control.
Addison-Wesley New York, 1989.
(3rd edition 2006)
Kinematic map, Jacobian, optimality principle of inverse kinematics, singularities, configura-
1:13
tion/operational/null space, multiple simultaneous tasks, special task variables, trajectory interpolation, motion profiles; 1D point mass, damping & oscillation, PID, general dynamic systems,

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