Introduction
Background
Novel room categories
Evaluation
Conclusion
Detecting Unknown High-level Concepts in
Semantic Mapping with Mobile Robots
Nikolaus Demmel
Kungliga Tekniska högskolan – Technische Universität München
May 9, 2012
Supervision
Andrzej Pronobis
Assoc. Prof. Patric Jensfelt
Nikolaus Demmel
Novelty Detection for Place Categorization
Dejan Pangercic
Prof. Michael Beetz
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Dora
Bathroom
Where should I look
for this book...
Anteroom
Hallway
Of fice
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Motivation
Title
Detecting Unknown High-level Concepts in Semantic Mapping
with Mobile Robots
Content
Novelty Detection for Place Categorization
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Motivation
Place categorization is important
Dora, make me
a sandwich!
What? Make it
yourself.
sudo make me
a sandwich!
Okay.
I need to find
a kitchen...
Planning
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Motivation
Place categorization is important
Dora, make me
a sandwich!
Dora, where are
my keys?
What? Make it
yourself.
In the office.
sudo make me
a sandwich!
Okay.
I need to find
a kitchen...
Planning
Nikolaus Demmel
Novelty Detection for Place Categorization
Human–robot interaction
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Motivation
Place categorization is important
Novelty detection is important
Robust behavior in changing environments.
Life-long operation.
Dora, make me
a sandwich!
Dora, where are
my keys?
What? Make it
yourself.
In the office.
sudo make me
a sandwich!
Okay.
I need to find
a kitchen...
Planning
Nikolaus Demmel
Novelty Detection for Place Categorization
Human–robot interaction
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Outline
1 Introduction
Motivation
Approach
2 Background
3 Novel room categories
Theory
Application
4 Evaluation
Experiments
Discussion
5 Conclusion
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Approach
Approach
Previous work by André Susano Pinto
We implement it on Dora
Idea
lower likelihood of observations = higher likelihood of novelty
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Background
Probabilistic Graphical Models
A
A
B
A
C
Bayesian Networks
(directed edges)
Nikolaus Demmel
Novelty Detection for Place Categorization
B
C
B
Markov Random
Fields
(undirected edges)
C
Chain Graphs
(both)
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Background
Place categorization (Pronobis, ICRA12)
metric map
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Background
Place categorization (Pronobis, ICRA12)
metric map
topological map
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Background
Place categorization (Pronobis, ICRA12)
metric map
topological map
room segmentation
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Background
Place categorization (Pronobis, ICRA12)
metric map
topological map
room segmentation
conceptual map
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Background
Place categorization (Pronobis, ICRA12)
metric map
topological map
room segmentation
conceptual map
place properties
shape
size
appearance
SVMs → distributions
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Background
Place categorization (Pronobis, ICRA12)
metric map
topological map
room segmentation
conceptual map
place properties
shape
size
appearance
SVMs → distributions
observation models
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Background
Novelty detection
Definition
Novelty detection is the identification of new or unknown
data or signals that a machine learning system is not
aware of during learning. — Markou and Singh
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Background
Novelty detection
Definition
Novelty detection is the identification of new or unknown
data or signals that a machine learning system is not
aware of during learning. — Markou and Singh
Surveys: Markou and Singh (2003), Chandola et al. (2009)
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Background
Novelty detection
Definition
Novelty detection is the identification of new or unknown
data or signals that a machine learning system is not
aware of during learning. — Markou and Singh
Surveys: Markou and Singh (2003), Chandola et al. (2009)
Applications
Fraud detection
Intrusion detection
Place categorization
(PLISS)
...
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Background
Novelty detection
Definition
Novelty detection is the identification of new or unknown
data or signals that a machine learning system is not
aware of during learning. — Markou and Singh
Surveys: Markou and Singh (2003), Chandola et al. (2009)
Applications
Fraud detection
Intrusion detection
Place categorization
(PLISS)
...
Nikolaus Demmel
Novelty Detection for Place Categorization
Techniques
Classification
Nearest neighbor
Clustering
Statistical
...
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Theory
Observation likelihoods
Novelty: N ∈ {n, n}
Observations: X
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Theory
Observation likelihoods
Novelty: N ∈ {n, n}
Observations: X
Nikolaus Demmel
Novelty Detection for Place Categorization
p(n | x)
≤
threshold
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Theory
Observation likelihoods
Novelty: N ∈ {n, n}
Observations: X
Nikolaus Demmel
Novelty Detection for Place Categorization
p(n | x)
≤
threshold
p(x | n) p(n)
p(x)
≤
threshold
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Theory
Observation likelihoods
Novelty: N ∈ {n, n}
Observations: X
Nikolaus Demmel
Novelty Detection for Place Categorization
p(n | x)
≤
threshold
p(x | n) p(n)
p(x)
≤
threshold
p(x | n)
p(x)
≤
threshold
p(n)
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Theory
Observation likelihoods
Novelty: N ∈ {n, n}
Observations: X
Nikolaus Demmel
Novelty Detection for Place Categorization
p(n | x)
≤
threshold
p(x | n) p(n)
p(x)
≤
threshold
p(x | n)
p(x)
≤
threshold
p(n)
p(x | n)
p(x)
≤
threshold0
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Theory
Observation likelihoods
Novelty: N ∈ {n, n}
Observations: X
Conditional likelihood: p(x | n)
Unconditional likelihood: p(x)
Nikolaus Demmel
Novelty Detection for Place Categorization
p(n | x)
≤
threshold
p(x | n) p(n)
p(x)
≤
threshold
p(x | n)
p(x)
≤
threshold
p(n)
p(x | n)
p(x)
≤
threshold0
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Theory
Observation likelihoods
Novelty: N ∈ {n, n}
Observations: X
Conditional likelihood: p(x | n)
Unconditional likelihood: p(x)
Novelty Score
p(x | n)
pc (x)
=
p(x)
pu (x)
Nikolaus Demmel
Novelty Detection for Place Categorization
p(n | x)
≤
threshold
p(x | n) p(n)
p(x)
≤
threshold
p(x | n)
p(x)
≤
threshold
p(n)
p(x | n)
p(x)
≤
threshold0
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Conditional likelihood (conceptual map)
C1
P1
X1
P2
X2
C2
P
P34
P5
P6
X4
X5
X6
X3
C3
...
C4
...
shape size appear.
shape size appear.
pc (x)
pu (x)
r4
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Conditional likelihood (conceptual map)
C1
P1
X1
P2
X2
C2
P
P34
P5
P6
X4
X5
X6
X3
C3
...
C4
...
shape size appear.
shape size appear.
pc (x)
pu (x)
Observations in other rooms: y
r4
Nikolaus Demmel
Novelty Detection for Place Categorization
pc (x) = p(x | y )
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Unconditional likelihood (independent uniform model)
Pn
Ĉ
...
pc (x)
pu (x)
P1
...
Xn
P2
P3
Xi
X1
X2
Nikolaus Demmel
Novelty Detection for Place Categorization
Pi
X3
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Unconditional likelihood (independent uniform model)
Pn
...
pc (x)
pu (x)
P1
...
Xn
Model properties
independent of each other.
P2
Novelty Detection for Place Categorization
P3
Xi
X1
X2
Nikolaus Demmel
Pi
X3
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Unconditional likelihood (independent uniform model)
Pn
...
pc (x)
pu (x)
P1
...
Xn
Model properties
independent of each other.
Model individual properties
as uniform.
Nikolaus Demmel
Novelty Detection for Place Categorization
P2
Pi
P3
Xi
X1
X2
X3
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Together
C1
C2
C3
C4
Pn
P5
P6
P2
X1
X2
X34
X
X5
X6
...
...
P1
P
P34
...
P1
...
Xn
shape size appear.
shape size appear.
P2
Pi
P3
Xi
X1
r4
X2
X
pc (c | y )
c
Nikolaus Demmel
Novelty Detection for Place Categorization
Y
i
P
pi
P
X3
pc (pi | c)φi (pi , xi )
pi
pu (pi )φi (pi , xi )
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Together
C1
C2
C3
C4
Pn
P5
P6
P2
X1
X2
X34
X
X5
X6
...
...
P1
P
P34
...
P1
...
Xn
shape size appear.
shape size appear.
P2
Pi
P3
Xi
X1
r4
X2
X
pc (c | y )
c
Nikolaus Demmel
Novelty Detection for Place Categorization
Y
i
P
pi
P
X3
pc (pi | c)φi (pi , xi )
pi
pu (pi )φi (pi , xi )
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Together
C1
C2
C3
C4
Pn
P5
P6
P2
X1
X2
X34
X
X5
X6
...
...
P1
P
P34
...
P1
...
Xn
shape size appear.
shape size appear.
P2
Pi
P3
Xi
X1
r4
X2
X
pc (c | y )
c
Nikolaus Demmel
Novelty Detection for Place Categorization
Y
i
P
pi
P
X3
pc (pi | c)φi (pi , xi )
pi
pu (pi )φi (pi , xi )
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Together
C1
C2
C3
C4
Pn
P5
P6
P2
X1
X2
X34
X
X5
X6
...
...
P1
P
P34
...
P1
...
Xn
shape size appear.
shape size appear.
P2
Pi
P3
Xi
X1
r4
X2
X
pc (c | y )
c
Nikolaus Demmel
Novelty Detection for Place Categorization
Y
i
P
pi
P
X3
pc (pi | c)φi (pi , xi )
pi
pu (pi )φi (pi , xi )
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Together
C1
C2
C3
C4
Pn
P5
P6
P2
X1
X2
X34
X
X5
X6
...
...
P1
P
P34
...
P1
...
Xn
shape size appear.
shape size appear.
P2
Pi
P3
Xi
X1
r4
X2
X
pc (c | y )
c
Nikolaus Demmel
Novelty Detection for Place Categorization
Y
i
P
pi
P
X3
pc (pi | c)φi (pi , xi )
pi
pu (pi )φi (pi , xi )
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Together
C1
C2
C3
C4
Pn
P5
P6
P2
X1
X2
X34
X
X5
X6
...
...
P1
P
P34
...
P1
...
Xn
shape size appear.
shape size appear.
P2
Pi
P3
Xi
X1
r4
X2
X
pc (c | y )
c
Nikolaus Demmel
Novelty Detection for Place Categorization
Y
i
P
pi
P
X3
pc (pi | c)φi (pi , xi )
pi
pu (pi )φi (pi , xi )
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Application
Thresholds
How to pick a threshold?
hand picked for our experiments
future work: more systematic with test-set and validation-set
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Experiments
Experiment 1
Increasing number of places
10
hallway (not novel)
hallway (novel)
8
6
log10(ν ⋆ )
4
2
0
−2
−4
−6
1
2
3
4
5
6
7
8
9
10
number of places
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Experiments
Experiment 2
Topological map
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Experiments
Experiment 2
Leave-one-out – Offices
Experiment 2 (no objects)
3.5
none
doubleoffice
professorsoffice
singleoffice
offices
3
2.5
log10 (ν⋆ )
2
1.5
1
0.5
0
−0.5
−1
r0 (DO, 5)
Nikolaus Demmel
Novelty Detection for Place Categorization
r7 (SO, 3)
r8 (PO, 4) r10 (SO, 2) r11 (PO, 3) r12 (PO, 2) r13 (PO, 2)
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Experiments
All rooms
Experiment 2 (no objects)
5
none
anteroom
bathroom
hallway
labs
offices
4
3
log10 (ν⋆ )
2
1
0
−1
−2
r0 (DO, 5)
r1 (HW, 30)
r3 (AR, 2)
r4 (BR, 2)
Nikolaus Demmel
Novelty Detection for Place Categorization
r5 (BR, 1)
r6 (RL, 4)
r7 (SO, 3)
r8 (PO, 4)
r9 (RL, 3)
r10 (SO, 2)
r11 (PO, 3)
r12 (PO, 2)
r13 (PO, 2)
r14 (RL, 3)
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Discussion
Evaluation
Works well...
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Discussion
Evaluation
Works well...
Nikolaus Demmel
Novelty Detection for Place Categorization
but what does it show?
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Discussion
Evaluation
Works well...
but what does it show?
Strong novelty cue from appearance due to leave-one-out.
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Discussion
Evaluation
Works well...
but what does it show?
Strong novelty cue from appearance due to leave-one-out.
Next step: Implement novelty detection for the appearance
classifier.
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Discussion
Future work
Novelty cue from appearance.
Systematic threshold selection.
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Discussion
Future work
Novelty cue from appearance.
Systematic threshold selection.
Object observations.
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Discussion
Future work
Novelty cue from appearance.
Systematic threshold selection.
Object observations.
Reaction to novelty. Changing Dora’s behavior.
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Recap: What did I do?
Novelty detection for place categorization.
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Recap: What did I do?
Novelty detection for place categorization.
Idea: Observation likelihood (existing work).
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Recap: What did I do?
Novelty detection for place categorization.
Idea: Observation likelihood (existing work).
Derived: Observation likelihoods on chain graph.
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Recap: What did I do?
Novelty detection for place categorization.
Idea: Observation likelihood (existing work).
Derived: Observation likelihoods on chain graph.
Implemented: Novelty score for Dora, auxiliary components.
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Recap: What did I do?
Novelty detection for place categorization.
Idea: Observation likelihood (existing work).
Derived: Observation likelihoods on chain graph.
Implemented: Novelty score for Dora, auxiliary components.
Code: Partially upstream. Rest will be merged in a branch.
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Recap: What did I do?
Novelty detection for place categorization.
Idea: Observation likelihood (existing work).
Derived: Observation likelihoods on chain graph.
Implemented: Novelty score for Dora, auxiliary components.
Code: Partially upstream. Rest will be merged in a branch.
Evaluated: Leave-one-out experiments with Dora.
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Recap: What did I do?
Novelty detection for place categorization.
Idea: Observation likelihood (existing work).
Derived: Observation likelihoods on chain graph.
Implemented: Novelty score for Dora, auxiliary components.
Code: Partially upstream. Rest will be merged in a branch.
Evaluated: Leave-one-out experiments with Dora.
Next step: Novelty for appearance.
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München
Introduction
Background
Novel room categories
Evaluation
Conclusion
Thank you
for listening!
Questions?
Nikolaus Demmel
Novelty Detection for Place Categorization
Kungliga Tekniska högskolan – Technische Universität München