Robot programming by demonstration: a probalistic approach
'Robot Programming by Demonstration' explores user-friendly means of teaching new skills to robots. This text focuses on two generic questions of what to imitate and how to imitate, which are concerned, respectively, with the problem of the extraction of the essential features of a task.
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Lausanne
EPFL [u.a.]
2009
|
| Edition: | 1. ed. |
| Series: | Engineering sciences : micro- and nanotechnology
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Summary: | 'Robot Programming by Demonstration' explores user-friendly means of teaching new skills to robots. This text focuses on two generic questions of what to imitate and how to imitate, which are concerned, respectively, with the problem of the extraction of the essential features of a task. |
| Item Description: | Ref. p. 201 - 220 |
| Physical Description: | X, 222 Seiten Illustrationen, Diagramme |
| ISBN: | 9781439808672 1439808678 9782940222315 |
Staff View
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| 490 | 0 | |a Engineering sciences : micro- and nanotechnology | |
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| 520 | |a 'Robot Programming by Demonstration' explores user-friendly means of teaching new skills to robots. This text focuses on two generic questions of what to imitate and how to imitate, which are concerned, respectively, with the problem of the extraction of the essential features of a task. | ||
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Record in the Search Index
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| adam_text |
CONTENTS
ACKNOWLEDGMENT
v
1
INTRODUCTION
1
1.1
Contributions
.
З
1.2
Organization of the book
. 3
1.3
Review of Robot Programming by Demonstration (PBD)
. 4
1.3.1
The birth of programmable machines
. 4
1.3.2
Early work of PbD in software development
. 5
1.3.3
Early work of PbD in robotics
. 6
1.3.4
Toward the use of machine learning techniques
in PbD
. 7
1.3.5
From a simple copy to the generalization of a skill
. 9
1.3.6
From industrial robots to service robots and
humanoids
. 10
1.3.7
From a purely engineering perspective to an
interdisciplinary approach
. 11
1.4
Current state of the art in PbD
. 12
1.4.1
Human-robot interfaces
. 12
1.4.2
Learning skills
. 14
1.4.3
Incremental teaching methods
. 22
1.4.4
Human-robot interaction in PbD
. 23
1.4.5
Biologically-oriented learning approaches
. 26
2
SYSTEM ARCHITECTURE
31
2.1
Illustration of the proposed probabilistic approach
. 31
2.2
Encoding of motion in a Gaussian Mixture Model (GMM)
. 34
2.2.1
Recognition, classification and evaluation of a
reproduction attempt
. 35
2.3
Encoding of motion in Hidden Markov Model
(HMM)
. 35
2.3.1
Recognition, classification and evaluation of a
reproduction attempt
. 37
2.4
Reproduction through Gaussian Mixture Regression (GMR)
. . 38
2.5
Reproduction by considering multiple constraints
. 44
2.5.1
Direct computation method
. 44
2.5.2
Method based on optimization of a
metric of imitation
. 45
Contents
2.6
Learning of model parameters
. 47
2.6.1
Batch learning of the GMM parameters
. 47
2.6.2
Batch learning of the
HMM
parameters
. 49
2.6.3
Incremental learning of the GMM parameters
. 51
2.7
Reduction of dimensionality and latent space projection
. 55
2.7.1
Principal Component Analysis (PCA)
. 56
2.7.2
Canonical Correlation Analysis (CCA)
. 57
2.7.3
Independent Component Analysis
(ICA)
. 57
2.7.4
Discussion on the different projection techniques
. 58
2.8
Model selection and initialization
. 60
2.8.1
Estimating the number of
Gaussiane
based on the
Bayesian Information Criterion
(BIC)
. 60
2.8.2
Estimating the number of
Gaussiane
based on
trajectory curvature segmentation
. 61
2.9
Regularization of GMM parameters
. 64
2.9.1
Bounding covariance matrices during
estimation of GMM
. 64
2.9.2
Single mode restriction during reproduction
through GMR
. 64
2.9.3
Temporal alignment of trajectories through
Dynamic Time Warping (DTW)
. 67
2.10
Use of prior information to speed up the learning process
. 69
2.11
Extension to mixture models of varying density distributions
. 71
2.11.1
Generalization of binary signals through a
Bernoulli Mixture Model (BMM)
. 71
2.12
Summary of the chapter
. 73
COMPARISON AND OPTIMIZATION OF THE PARAMETERS
75
3.1
Optimal reproduction of trajectories through
HMM
and GMM/GMR
. 75
3.1.1
Experimental setup
. 75
3.1.2
Experimental results
. 79
3.2
Optimal latent space of motion
. 87
3.2.1
Experimental setup
. 87
3.2.2
Experimental results
. 89
3.3
Optimal selection of the number of Gaussians
. 92
3.3.1
Experimental setup
. 93
3.3.2
Experimental results
. 93
3.4
Robustness evaluation of the incremental learning process
. 94
3.4.1
Experimental setup
. 95
3.4.2
Experimental results
. 97
HANDLING OF CONSTRAINTS IN JOINT SPACE
AND TASK SPACE
101
4.1
Inverse kinematics
. 101
4.1.1
Local solutions
. 102
4.1.2
Extending inverse kinematics solutions to a statistical
framework
. 104
Contents
4.2
Handling of task constraints in joint space-experiment with
industrial robot
. 106
4.2.1
Experimental setup
. 109
4.2.2
Experimental results
. 113
4.3
Handling of task constraints in latent space-experiment with
humanoid robot
. 116
4.3.1
Experimental setup
. 120
4.3.2
Experimental results
. 120
5
EXTENSION TO DYNAMICAL SYSTEM AND HANDLING OF
PERTURBATIONS
129
5.1
Proposed dynamical system
. 130
5.1.1
Extension to motions containing pauses and loops
. 133
5.2
Influence of the dynamical system parameters
. 135
5.3
Experimental setup
. 135
5.3.1
Illustration of the problem
. 138
5.3.2
Handling of multiple landmarks
. 139
5.3.3
Handling of inverse kinematics
. 140
5.4
Experimental results
. 141
6
TRANSFERRING SKILLS THROUGH ACTIVE
TEACHING METHODS
147
6.1
Experimental setup
. 148
6.2
Experimental results
. 151
6.2.1
Experiment
1:
learning bimanual gestures
. 151
6.2.2
Experiment
2:
learning to stack objects
. 152
6.2.3
Experiment
3:
learning to move chess pieces
. 158
6.3
Roles of an active teaching scenario
. 166
6.3.1
Insights from psychology
. 166
6.3.2
Insights from developmental sciences
. 167
6.3.3
Insights from sociology
. 169
6.3.4
Insights from sports science
. 169
7
USING SOCIAL CUES TO SPEED UP THE
LEARNING PROCESS
171
7.1
Experimental setup
. 173
7.1.1
Use of head/gaze information as priors
. 173
7.1.2
Use of vocal information as priors
. 176
7.2
Experimental results
. 178
8
DISCUSSION, FUTURE WORK AND CONCLUSIONS
181
8.1
Advantages of the proposed approach
. 181
8.1.1
Advantages of using motion sensors to track gestures
. . 181
8.1.2
Advantages of the
HMM
representation for
imitation learning
. 183
8.1.3
Advantages of the GMR representation for regression
. 185
Contents
8.2
Failures and limitations of the proposed approach
. 188
8.2.1
Loss of important information through PCA
. 188
8.2.2
Failures at learning incrementally the
GMM parameters
. 189
8.2.3
Failures at extending a skill to a context that is
too dissimilar to the ones encountered
. 193
8.3
Further issues
. 194
8.3.1
Toward combining exploration and imitation
. 194
8.3.2
Toward a joint use of discrete and continuous
constraints
. 195
8.3.3
Toward predicting the outcome of a demonstration
. . . 198
8.4
Final words
. 198
REFERENCES
201
INDEX
221
ROBOT
PROGRAMMING
BY DEMONSTRATION:
A PROBABILISTIC APPROACH
Sylvain Calinon
Robot Programming by Demonstration (PbD) examines methods by which a robot
learns new skills through human guidance. PbD takes inspiration from the way humans
learn new skills by imitation, thereby developing methods by which new skills can be
transmitted to a robot. In industrial settings, the goal is to reduce the time and costs re¬
quired to program the robot, where the rationale is to create, modify or assemble a prod¬
uct in a very rapid way without requiring help from an expert in robotics. PbD is
perceived as particularly useful when it comes to service robots, i.e., robots deemed to
work in direct collaboration with humans. In this case, methods for PbD go beyond
transferring skills and offer new ways for the robot to interact with the human, from
being capable of recognizing people's motion to predicting their intentions and sec¬
onding them in the accomplishment of complex tasks. The present work addresses both
challenges in investigating methods by which PbD is used to provide the robot with a
generic and adaptive model of control.
This book takes a practitioner's perspective and is relevant for anyone involved in ro¬
botics research or human-robot interaction, or who simply wants to discover and un¬
derstand how to transfer skills to robots by imitation. The findings presented in this
book emerge from various disciplines such as robot control, human-robot interaction,
applied machine learning and artificial intelligence. It also takes insights from develop¬
mental and cognitive psychology.
Sylvain
Caiinon (b.
1980]
is a research scientist at the Italian Institute of Technology
(
I IT),
Dept
of Advanced Robotics, and a visiting research scientist at the Learning Algorithms and Sys¬
tems Laboratory
(LASA),
Ecole polytechnique
íédérale de
Lausanne (EPFL).
He received a PhD
on robot programming by demonstration in
2007
from EPFL, which was awarded by the inter¬
national
Robotdalen
scientific award,
Asea
Brown Boveri (ABB) award and EPFL-Press distinc¬
tion. From
2007
to
2009,
Dr
Calinon was a postdoctoral research fellow at
LASA.
His research
interests cover robot programming by demonstration, learning by imitation and human-robot |
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| id | DE-604.BV035591237 |
| illustrated | Illustrated |
| indexdate | 2025-01-17T13:04:02Z |
| institution | BVB |
| isbn | 9781439808672 1439808678 9782940222315 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017646453 |
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| owner_facet | DE-703 DE-91 DE-BY-TUM DE-573 DE-739 |
| physical | X, 222 Seiten Illustrationen, Diagramme |
| publishDate | 2009 |
| publishDateSearch | 2009 |
| publishDateSort | 2009 |
| publisher | EPFL [u.a.] |
| record_format | marc |
| series2 | Engineering sciences : micro- and nanotechnology |
| spelling | Calinon, Sylvain 1980- Verfasser (DE-588)1035794896 aut Robot programming by demonstration a probalistic approach Sylvain Calinon 1. ed. Lausanne EPFL [u.a.] 2009 X, 222 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Engineering sciences : micro- and nanotechnology Ref. p. 201 - 220 'Robot Programming by Demonstration' explores user-friendly means of teaching new skills to robots. This text focuses on two generic questions of what to imitate and how to imitate, which are concerned, respectively, with the problem of the extraction of the essential features of a task. Robots / Programming Robots Programming Automatische Programmierung (DE-588)4143702-0 gnd rswk-swf Industrieroboter (DE-588)4026861-5 gnd rswk-swf Industrieroboter (DE-588)4026861-5 s Automatische Programmierung (DE-588)4143702-0 s DE-604 Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017646453&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Bayreuth application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017646453&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Calinon, Sylvain 1980- Robot programming by demonstration a probalistic approach Robots / Programming Robots Programming Automatische Programmierung (DE-588)4143702-0 gnd Industrieroboter (DE-588)4026861-5 gnd |
| subject_GND | (DE-588)4143702-0 (DE-588)4026861-5 |
| title | Robot programming by demonstration a probalistic approach |
| title_auth | Robot programming by demonstration a probalistic approach |
| title_exact_search | Robot programming by demonstration a probalistic approach |
| title_full | Robot programming by demonstration a probalistic approach Sylvain Calinon |
| title_fullStr | Robot programming by demonstration a probalistic approach Sylvain Calinon |
| title_full_unstemmed | Robot programming by demonstration a probalistic approach Sylvain Calinon |
| title_short | Robot programming by demonstration |
| title_sort | robot programming by demonstration a probalistic approach |
| title_sub | a probalistic approach |
| topic | Robots / Programming Robots Programming Automatische Programmierung (DE-588)4143702-0 gnd Industrieroboter (DE-588)4026861-5 gnd |
| topic_facet | Robots / Programming Robots Programming Automatische Programmierung Industrieroboter |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017646453&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017646453&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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