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- Robotics and Robot Applications - RRA
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- Introduction - ( 3 - 4 )
- Kinematics - ( 5 - 13 )
- Path planning - ( 14 - 31 )
- Probability basics - ( 32 - 42 )
- control theory - ( 43 - 49 )
- Practical: A 2-wheeled racer - ( 50 - 53 )
- Reinforcement learning in robotics - ( 54 - 60 )
- Skipped this term-grasping - ( 61 - 63 )
- Skipped this term-legged - ( 64 - 78 )
- Topic list - ( 79 - 79 )

Topic:

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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