Introduction Course Format, Schedule & Grading Overview ...130.243.105.49/Research/Learning/rkg_dir/rc_2016/lecture_1.pdf · rc_2016/grading.pdf. Introduction Course Format, Schedule

Introduction

Course Format,Schedule & Grading

OverviewWhat’s it All About . . . ?

The Big Picture

Wrap-up

References

1.1

Lecture 1IntroductionRobot ControlMar. 29, 2016

Robert KrugÖrebro University

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About Me . . .

Robert Krug

Postdoc at the Mobile Robotics & Olfaction Lab,Örebro UniversityResearch domains

Autonomous robot grasping/manipulation

Online motion planning/control

Office: T1220Phone: (+46/0) 19 - 30 3499Email: [email protected]

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Agenda

1 Course Format, Schedule & Grading

2 OverviewWhat’s it All About . . . ?The Big PictureWrap-up

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Disclaimer

Dimitar Dimitrov, Manipulation and Controlcourse, 2011.www.aass.oru.se/Research/Learning/drdv_dir/course_mc_2011.html

Magnus Egerstedt, Control ofMobile Robots course, 2015www.coursera.org/learn/mobile-robot

B. Siciliano, L. Sciavicco, L. Villani and G. Oriolo,2009, Robotics: modelling, planning and control,Springer Science & Business Media, ISBN:978-1-84628-642-1, 632 pages

www.aass.oru.se/Research/Learning/drdv_dir/course_mc_2011.html

www.aass.oru.se/Research/Learning/drdv_dir/course_mc_2011.html

www.coursera.org/learn/mobile-robot

www.coursera.org/learn/mobile-robot

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Course Webpage

http://www.aass.oru.se/Research/mro/rkg_dir/course_rc_2016.html

Weekly updated lecture slides

Weekly updated exercises

Support material

Schedule

Course format & grading specification

Literature list



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Course Format

A (hybrid) Flipped Classroom approach

https://learningsciences.utexas.edu/teaching/flipping-a-class



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Course Format

A (hybrid) Flipped Classroom approach

Lectures 2h

Demos & Discussions 2h

Exercises 2h

Weekly schedule: in total 9 weekly sessions á 6h

Lectures→ intuition

Self study of the “hard” concepts using reading lists

Q & A→ addressing submitted questions, walk throughpractical examples

Exercises→ students solve given assignments

last 3 weeks→ mini-project

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Content & Schedule

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Content & Schedule

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Content & Schedule

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Content & Schedule

http://www.aass.oru.se/Research/mro/rkg_dir/rc_2016/schedule.pdf

The greater danger for most of us lies not in setting our aim toohigh and falling short; but in setting our aim too low, andachieving our mark.- Michelangelo -



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What’s Not in the Course?

Classical Control [1]→ Bode methods, root locus,Laplace/Fourier transforms, analysis in frequency domain;(we use Modern Control based on ODE’s)

System Identification

Signal processing

Stochastic control

Adaptive control

(Full) optimal control

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Examination and Grading

Course grades: Fail (U), Pass (G) or Pass with Distinction (VG)

1 written “filter” exam→ U or G2 weekly exercises→ U, G or VG

Matlab code + brief, written report

7 exercises × 22 points/exercise = 154 points

each can be resubmitted once after feedback3 “publication-style” final project report,

demo and presentation→ U, G or VG150 points according to grading rubric







Grading: VG = 308 - 230 pts, G = 229 - 150 pts, U = 149 - 0 pts

exam, exercises and project need to be passed separately

http://www.aass.oru.se/Research/mro/rkg_dir/rc_2016/grading.pdf



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1 written “filter” exam→ U or G

2 weekly exercises→ U, G or VG



each can be resubmitted once after feedback

3 “publication-style” final project report,demo and presentation→ U, G or VG

150 points according to grading rubric






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Control Theory is about . . . ?

System: Something that changes over time

Control: Influence to achieve desired behavior

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Control system ingredients

State x : Rep. of what the system is currently doing

Dynamics x : Description of the state’s change

Process

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Control u: Influence on the system

ProcessController

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Reference r : Rep. of what the system should do

ProcessController

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Reference r : Rep. of what the system should do

Output y : (Partial) measurements of the system

Feedback: Mapped output

ProcessController

Feedbackmapping

+

-

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A Bit of Context . . .

Artificial Intelligence (AI): make a sequence of decisionsover time so as to achieve certain goals [8]

fits the bill of a control problem . . .

. . . but AI is traditionally more focused on problem solving

How does Control Theory relate to / differ from:

AI / Planning,

Machine Learning?

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A Bit of Context . . .

Artificial Intelligence (AI): make a sequence of decisionsover time so as to achieve certain goals [8]

fits the bill of a control problem . . .

. . . but AI is traditionally more focused on problem solving

How does Control Theory relate to / differ from:

AI / Planning,

Machine Learning?

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Control Theory vs AI

What do the gurus (Peter Norvig & Sebastian Thrun) say?

https://www.udacity.com/course/intro-to-artificial-intelligence--cs271



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Control Theory vs AI

According to Sutton [8]:

AI

Solve hard problems

“Perfect model disease”(toy domains, block worlds,. . . )

No understanding of thecontrolled system

Control Theory

Clear, rigorous

Based on proven concepts

Guarantees (convergence,stability, . . . )

Limited

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A (hopefully) Illustrative Exemplary Problem

Robot Motion Planning: Find a path that moves a point-robotgradually from start to goal while avoiding obstacles

1st (ofmany) AI options: Path Planning via Search

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1st (of many) AI options: Path Planning via Search

World represented on discrete grid

Breadth First Search: explore space in layers . . .

. . . and label cells according to a distance metric

Once the goal cell is explored . . .

. . . trace back to find the minimum-distance path

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Do you see any issues with this approach?

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Always finds optimal solution

World discretization error

What about the robot’s physical properties(kinematic/dynamic constraints)?

Computational effort

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Another option: Find a Random Solution via Sampling

Grow a tree by random sampling (RRT [6])

Valid samples can be connected (collision checking)

Invalid samples are rejected

Once the goal node can be connected . . .

. . . a path is extracted from the tree

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Where do you see the pros/cons of this approach?

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Finds a solution if given enough time

Solution is random

What about the robot’s physical properties(kinematic/dynamic constraints)?

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http://aass.oru.se/~fll/videos/box_middle2.mpg [5]

http://aass.oru.se/~fll/videos/box_middle2.mpg

http://aass.oru.se/~fll/videos/box_middle2.mpg

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A Control Approach to Motion Planning

Assume our robot is governed by the (super-unrealistic for anyrobot) dynamics:

x = f (x , t) =[q1q2

]︸︷︷︸

x

=

[λ1 00 λ2

]︸︷︷︸

A

[q1q2

]︸︷︷︸

x

(1)

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Add (constant) control u:

x = f (x ,u, t) =[q1q2

]︸︷︷︸

x

=

[λ1 00 λ2

]︸︷︷︸

A

[q1q2

]︸︷︷︸

x

+

[−λ1g1−λ2g2

]︸︷︷︸

u

(1)

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Recall: x = what system is doing; x = description of change

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State evolution is governed by the map x = f (x ,u, t)

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State evolution is governed by the map x = f (x ,u, t)

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The dynamical system x = f (x ,u, t) covers the whole statespace→ built-in disturbance rejection

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You cheated!!! What about obstacles? → e.g. addrepelling potential fields [4] or constraints [3]

Control can be seen as real-time, reactive, continuousplanning

Local, greedy approach→ remedied by global optimalcontrol/trajectory optimization (costly!)

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Wrap-up

AIclassically based onproblem-solving:

Solving puzzles

Playing board-games

Control Theory

Classically based ontheorem-proof mathapproach:

Low-level actuatorcontrol (e. g. DC-motorvelocity control)

Often no clear boundaries between control, AI, Learning!

Cross-disciplinaryMotion planning via control

Learning controllers from data [2, 7]

Reinforcement Learning (= adaptive control [9])

. . .

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Wrap-up

AIclassically based onproblem-solving:

Solving puzzles

Playing board-games

Control Theory

Classically based ontheorem-proof mathapproach:

Low-level actuatorcontrol (e. g. DC-motorvelocity control)

Often no clear boundaries between control, AI, Learning!

Cross-disciplinaryMotion planning via control

Learning controllers from data [2, 7]

Reinforcement Learning (= adaptive control [9])

. . .

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Where Are We Today?

DARPA Robotics Challenge 2015

https://www.youtube.com/watch?v=g0TaYhjpOfo

https://www.youtube.com/watch?v=g0TaYhjpOfo

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Where Are We Today?

here . . . . . . or here?

“I think that the control viewpoint is now much more profitablethan the problem solving one, and that control should be thecenterpiece of AI and machine learning research.”1

Bottomline: A robot is a physical system acting in a physicalworld→ at some (ideally across all) architecture levels youhave to think about control.

1Richard S. Sutton. “Artificial intelligence as a control problem: Commentson the relationship between machine learning and intelligent control”. In: Proc.IEEE Int. Symp. on Intell. Control. 1988, pp. 500–507.

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Where Are We Today?





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Where Are We Today?





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Reading List

Dimitar Dimitrov, Manipulation and Control, Handout 1:http://www.aass.oru.se/Research/Learning/drdv_dir/mc2011/Lecture_1.pdf

Supplemental:

Nathan Ratliff, Mathematics for Intelligent Systems, LinearAlgebra I: Matrix Representations of Linear Transforms:http://www.nathanratliff.com/pedagogy/mathematics-for-intelligent-systems

http://www.aass.oru.se/Research/Learning/drdv_dir/mc2011/Lecture_1.pdf

http://www.aass.oru.se/Research/Learning/drdv_dir/mc2011/Lecture_1.pdf

http://www.nathanratliff.com/pedagogy/mathematics-for-intelligent-systems

http://www.nathanratliff.com/pedagogy/mathematics-for-intelligent-systems

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You don’t get away that easy . . .,

Exit Ticket - one minute response

Most important thing you learned today?

Main unanswered question?

Muddiest point?

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Gene F. Franklin, David J. Powell, andAbbas Emami-Naeini. Feedback Control of DynamicSystems. 4th ed. Prentice Hall, 2002.

Auke J. Ijspeert, Jun Nakanishi, and Stefan Schaal.“Movement imitation with nonlinear dynamical systems inhumanoid robots”. In: Proc. IEEE Int. Conf. Rob. Autom.(ICRA). Vol. 2. 2002, pp. 1398–1403.

Oussama Kanoun, Florent Lamiraux, andPierre-Brice Wieber. “Kinematic control of redundantmanipulators: Generalizing the task-priority framework toinequality task”. In: IEEE Trans. Rob. (T-RO) 27.4 (2011),pp. 785–792.

Oussama Khatib. “Real-time obstacle avoidance formanipulators and mobile robots”. In: Int. J. Rob. Res.(IJRR) 5.1 (1986), pp. 90–98.

Fabien Lagriffoul et al. “Efficiently combining task andmotion planning using geometric constraints”. In: Int. J.Rob. Res. (IJRR) 33.14 (2014), pp. 1726–1747.

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Steven M. LaValle. Planning algorithms. Cambridgeuniversity press, 2006.

Sergey Levine, Nolan Wagener, and Pieter Abbeel.“Learning contact-rich manipulation skills with guidedpolicy search”. In: Proc. IEEE Int. Conf. Rob. Autom.(ICRA). 2015, pp. 156–163.

Richard S. Sutton. “Artificial intelligence as a controlproblem: Comments on the relationship between machinelearning and intelligent control”. In: Proc. IEEE Int. Symp.on Intell. Control. 1988, pp. 500–507.

Richard S. Sutton, Andrew G. Barto, andRonald J. Williams. “Reinforcement learning is directadaptive optimal control”. In: IEEE Control Systems 12.2(1992), pp. 19–22.

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Introduction Course Format, Schedule & Grading Overview ...130.243.105.49/Research/Learning/rkg_dir/rc_2016/lecture_1.pdf · rc_2016/grading.pdf. Introduction Course Format, Schedule