statistical learning in robotics state-of-the-art, challenges and opportunities

Sebastian ThrunCarnegie Mellon University

Statistical Learning in RoboticsState-of-the-Art, Challenges and Opportunities

Sebastian Thrun Carnegie Mellon University ICML July 10-12, 2002

7 Open Problems

Estimation andLearning In

Robotics

RoboticsResearch Today



Robotics Yesterday


Robotics Today


Robotics Tomorrow?

Thanks toT. Dietterich


Robotics @ CMU, 1992


Robotics @ CMU 1996

With: RWI / iRobot, Hans Nopper


Robotics @ CMU/UBonn, 1997

with W. Burgard, A.B. Cremers, D. Fox, D. Hähnel, G. Lakemeyer, D. Schulz, W. Steiner



with M. Beetz, M. Bennewitz, W. Burgard, A.B. Cremers, F. Dellaert, D. Fox, D. Hähnel, C. Rosenberg, N. Roy, J. Schulte, D. Schulz


7 Open Problems



Robotics


The Robot Localization Problem

• Position tracking (error bounded)• Global localization (unbounded error)• Kidnapping (recovery from failure)

?


Probabilistic Localization

),,|(),,,|( 010010 muzxpmuzxzp ttttttt Bayes

[Kalman 60, Rabiner 85]

x = statet = timem = mapz = measurementu = control

Markov),,|(),|( 010 muzxpmxzp ttttt

101010101 ),,|(),,,|(),|( tttttttttt dxmuzxpmuzxxpmxzp

Markov

1101011 ),,|(),|(),|( ttttttttt dxmuzxpuxxpmxzp

),,|( 00 muzxp ttt

laser data p(z|x,m)map m

xt-1

ut

p(xt|xt-1,ut)


What is the Right Representation?

Multi-hypothesis

[Weckesser et al. 98], [Jensfelt et al. 99]

Particles

[Kanazawa et al 95] [de Freitas 98][Isard/Blake 98] [Doucet 98]

Kalman filter

[Schiele et al. 94], [Weiß et al. 94], [Borenstein 96], [Gutmann et al. 96, 98], [Arras 98]

[Nourbakhsh et al. 95], [Simmons et al. 95], [Kaelbling et al. 96], [Burgard et al. 96], [Konolige et al. 99]

Histograms(metric, topological)


Monte Carlo Localization (MCL)

With: Wolfram Burgard, Dieter Fox, Frank Dellaert


Implications for Planning & Control

MDP Planner POMDP Planner

With N. Roy


Monte Carlo Localization

With:

Frank

Dellaert


Learning Mapsaka Simultaneous Localization and Mapping (SLAM)

70 m


Learning Maps

110101100 ),,|(),|(),|(),,|( tttttttttttt dxmuzxpuxxpmxzpmuzxp

106 dimensions 3 dimensions

Localization:

111010111100 ),|,(),,|,(),|(),|,( tttttttttttttttttt dmdxuzmxpumxmxpmxzpuzmxp

110101100 ),|,(),|(),|(),|,( tttttttttttt dxuzmxpuxxpmxzpuzmxp


Learning Maps with Extended Kalman Filters

2

2

2

2

2

2

2

1

11

21

21

21

21

2222221

1111211

,),|,(

yxlll

yyxyylylyl

xxyxxlxlxl

lylxllllll

lylxllllll

lylxllllll

Nttt

N

N

N

NNNNNN

N

N

y

x

l

l

l

uzmxp

[Smith, Self, Cheeseman, 1990]


Kalman Filter Mapping: O(N2)


Can We Do the Same WithParticle Filters?

robot poses and maps

),|,( 00 ttt uzmxp

sample map + pose


N

ntttntttttt uzsmpuzspuzsmp

1000000000 ),,|(),|(),|,(

Mapping: Structured Generative Model

s1 s2 st

u2 ut

m2

m1

z1

z2

s3

u3

z3

zt

. . .

Landmark

robot pose

control

measurement

With K. Murphy, B. Wegbreit and D. Koller


Rao-Blackwellized Particle Filters

landmark n=2

…

),|(~ 00][

ttti

t uzxpx

landmark n=Nlandmark n=1

landmark n=2

…landmark n=Nlandmark n=1

),,|( 00][

tti

tn uxmp

robot poses

[Murphy 99, Montemerlo 02]

N

ntttntttttt uzsmpuzspuzsmp

1000000000 ),,|(),|(),|,(


Ben Wegbreit’s Log-Trick

8,87,7

k 3 ?

FT

6,65,5

k 1 ?

FT

4,43,3

k 3 ?

FT

2,21,1

k 1 ?

FT

k 6 ?

FT

k 2 ?

FT

k 4 ?FT

[i][i] [i][i] [i][i] [i][i] [i][i] [i][i] [i][i][i][i] 8,87,7

n 7 ?

FT

6,65,5

n 5 ?

FT

4,43,3

n 3 ?

FT

2,21,1

n 1 ?

FT

n 6 ?

FT

n 2 ?

FT

n 4 ?FT

[i] [i][i] [i][i] [i][i] [i][i] [i][i] [i][i][i]

3,3

n 3 ?

FT

n 2 ?

FT

n 4 ?

F

T

[i][i]

new particle

old particle

Michael Montemerlo, Ben Wegbreit, Daphne Koller & Sebastian Thrun


Advantage of Structured PF Solution

Kalman: O(N2)

500 features

1,000,000 features

Moore’s Theorem: logN 30Experimental: M=250

Rao-B’ PFs: O(MlogN)

+ global uncertainty, multimodal+ non-linear systems+ sampling over data associations


3 Examples

Particles +Kalman filters Particles +

Particles

Particles +Point Estimators


Outdoor Mapping (no GPS)

With Juan Nieto, Jose Guivant, Eduardo Nebot, Univ of Sydney


With Juan Nieto, Jose Guivant, Eduardo Nebot, Univ of Sydney


Tracking Moving Features

With: Michael Montemerlo


Tracking Moving Entities Through Map Differencing


Map-Based People Tracking



Autonomous People Following



Indoor Mapping

Map: point estimators (no uncertainty) Lazy


Importance of Probabilistic Component

Non-probabilistic Probabilistic, with samples


Multi-Robot Exploration

DARPA TMR MarylandDARPA TMR Texas

With: Reid Simmons and Dieter Fox


Learning Object Models


Nearly Planar Maps

Idea: Exploit fact that buildings posses many planar surfaces Compacter models Higher Accuracy Good for capturing environmental change


Online EM and Model Selection

mostly planar mapraw data


Online EM and Model Selection

CMU Wean Hall Stanford Gates Hall


3D Mapping Result

With: Christian Martin


Combining Tracking and Mapping

With Dirk Hähnel, Dirk Schulz and Wolfram Burgard


Underwater Mapping (with University of Sydney)

With: Hugh Durrant-Whyte, Somajyoti Majunder, Marc de Battista, Steve Scheding




Robotics

7 Open Problems


Can We Learn Better Maps?

Stationary objects and moving objects, people

Motion characteristics, relational knowledge Less structured environments (jungle, underwater) In real-time


Can We Learn Control?

1t0..t0..t u)u,z|(: txp

Not an MDP Not discrete or low-dimensional Not knowledge-free


How Can We Learn in Context?

Goal of robotics is not … mapping classification clustering density estimation reward prediction …

But simply: Doing the right thing.


How can we exploit Domain Knowledge in Learning?

Test: Is hypothesis consistent with laws of geometry? laws of physics? conventional wisdom? …

Domain knowledge is your friend! ILP? “Lifelong” learning?


Can we Integrating Learning and Programming?

LearningProgramming

prob<int> x = {{10, 0.2}, {11, 0.8}};

prob<int> y = {{20, 0.5}, {21, 0.5}};

prob<int> z = x + y;

prob<double> f = neuroNet(y);

with Frank Pfenning, CMU


What Can We LearnFrom Biology?

Courtesy of Bill Skaggs, University of Pittsburgh


University of Pittsburgh School of Nursing

Prof. Jackie Dunbar-JacobProf. Sandy EngbergProf. Margo HolmProf. Deb LewisProf. Judy MatthewsProf. Barbara Spier

School of MedicineProf. Neil ResnickProf. Joan Rogers

Intelligent SystemsProf. Don Chiarulli

University of Pittsburgh Computer Science Prof. Martha Pollack

Carnegie Mellon University Computer Science, Robotics

Prof. Sebastian Thrun

Prof. Geoff Gordon

Human Computer Interaction Prof. Sara Kiesler

Financial Support National Science Foundation

$1.4M ITR Grant $3.2M ITR Grant

…And Can We Actually DoSomething Useful?


The Nursebot Project


Haptic Interface (In Development)


Wizard of Oz Studies

By Sara Kiesler, Jenn Goetz


Truly Useful….?

statistical learning in robotics state-of-the-art, challenges and opportunities

Documents

thrun et

robotics tomorrow

burgard et

wolfram burgard

learning mapslocalization

statistical learning

monte carlo localizationwith

dieter fox