Roboterlernen & ethische Fragen Wie werden wir assistiert arbeiten ? - Jochen Steil, Technische Universität Braunschweig, Institut für Robotik & ...
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Platzhalter für Bild, Bild auf Titelfolie hinter das Logo einsetzen
Roboterlernen & ethische Fragen
Wie werden
wollen wir assistiert arbeiten ?
Jochen Steil, Technische Universität Braunschweig,
Institut für Robotik & Prozessinformatik
www.robotik-bs.de
Institut für Robotik und Prozessinformatik 2
human-robot collaboration
humanoid digital, flexible
robots production systems
… with application in
Supervised learning
Daten,
Demonstrationen
Target
Demonstrations
Lyapunov Candidate
Industrie 4.0, digital society,
Position (x,y)
Target
Demonstrations
Target
Demonstrations
Lyapunov Candidate
Sollausgabe
Target
Demonstrations
Reproductions
Dynamic Flow
future of work
Lyapunov Candidate
Lernalgorithmus Fehler
Parameteranpassung Istausgabe
Target
Demonstrations
Reproductions
Dynamic Flow
Datenmodell Fig. 5. Estimates of the J-2-shape and respective Lyapunov candidates. The J-2-shape approximated without explicit stabilization (first column), with Lq
(second column), LP (third column), LELM as Lyapunov candidate (fourth column). The SEDS estimate (fifth column, first row). The stability conditions
Eingabe
Target
Demonstrations (parametrisierte in SEDS are derived based on a quadratic energy function [13] (fifth column, second row).
Funktion)
Reproductions
Dynamic Flow
due the high flexibility of the candidate function. Fig. 5 ELMs need an ex-post verification which is computationally
shows the estimations of the J-2-shape and their respective expensive. The experiments support the hypothesis that the
Neurally imprinted stable vector fields, A. Lemme, F. Reinhart, J. Steil, ESANN 2013, best paper award. Lyapunov candidates in addition to the second tabular of flexibility of the Lyapunov candidate become more important
Neural Learning of Stable Dynamical Systems based on Data-Driven Lyapunov Candidates, A. Lemme, F. Reinhart, J. Steil, IROS, 2013
Tab. I. The left column of the figure contains the estimation if the demonstrations are of higher complexity.
Fig. 5. Estimates of the J-2-shape and respective Lyapunov candidates. The J-2-shape approximated without explicit stabilization (first column), with Lq
result obtained for an ELM without regards to stability. The
(second column), LP (third column), LELM as Lyapunov candidate (fourth column).V.The SEDS estimateT(fifth
K INESTHETIC column,
EACHING OFfirst row). The stability conditions
I C UB
24.05.2017 | © Prof. J. Steil 2017 | Antrittsvorlesung | Roboterlernen: Science & Fiction data is accurately approximated
in SEDS are derived based on a quadratic but theenergy
targetfunction
is not reached
[13] (fifth column, second row).
We analyze the methods in a real world scenario involving
robot learning,
at the end of the reproduced trajectories. The plot also em-
phasizes that even reproductions starting in the vicinity of the the humanoid robot iCub [7] in addition to the experiments
Fig. 5. Estimates of the J-2-shape and respective Lyapunov candidates. The J-2-shape approximated without explicit stabilization demonstrations (first column),
are prone with
to Ldivergence.
q The second column discussed in the previous section. Such robots are typically
(second column), LP (third column), LELM as Lyapunov candidate (fourth column). The SEDS estimate (fifth column, first duerow).theThe high flexibility of the candidate function. Fig. 5 ELMs need an ex-post verification which is computationally
illustrates thestability
results conditions
for networks trained with respect to Lq . designed to solve service tasks in environments where a
in SEDS are derived based on a quadratic energy function [13] (fifth column, second row). shows the estimations of the J-2-shape and their respective expensive. The experiments support the hypothesis that the
It is shown that this Lyapunov candidate introduces a very high flexibility is required. Robust adaptability by means
Lyapunov candidates in addition to the second tabular of flexibility of the Lyapunov candidate become more important
strict form of stability, without respect to the demonstrations. of learning is thus a prerequisite for such systems. The
The reproductions are directly the
Tab. I. The left column of figure contains
converging towards the theattrac-
estimation if the setting
experimental demonstrations are inofFig.
is illustrated higher
6. Acomplexity.
human tutor
due the high flexibility of the candidate function. Fig. 5 ELMs need an ex-post verificationtor. which
result is
Thisobtained computationally
is due to for an ELM
the high without
violation of theregards to stability.
demonstrations by The V. K INESTHETIC T EACHING OF I C UB
shows the estimations of the J-2-shape and their respective expensive. The experiments support Ldata the
q close hypothesis
is accurately ofthat
to the start approximatedthe the but theColumn
demonstrations. target isthree
not ofreached
neural networks
Lyapunov candidates in addition to the second tabular of flexibility of the Lyapunov candidate Fig. become
at the end of
5 shows more
the important
theresults
reproducedfor LP .trajectories.
This Lyapunov Thecandidate
plot also em- We analyze the methods in a real world scenario involving
soft robotics
Tab. I. The left column of the figure contains the estimation if the demonstrations are of higher isphasizes
data-dependent
complexity. that evenbut still too limited
reproductions to capture
starting the full of the
in the vicinity the humanoid robot iCub [7] in addition to the experiments
result obtained for an ELM without regards to stability. The structure
demonstrations of the J-2 aredemonstrations.
prone to divergence. The fourth
The column
second of column discussed in the previous section. Such robots are typically
V. K INESTHETIC T EACHING the figureOF
illustrates I C UB the performance of the networks trained
results for networks trained with respect to Lq . designed to solve service tasks in environments where a
illustrates
the
data is accurately approximated but the target is not reached
We analyze the methods in a by
real L
world
ELM . The
scenario Lyapunov
involving candidate is strongly curved to
It is shown that this Lyapunov candidate introduces a very high flexibility is required. Robust adaptability by means
at the end of the reproduced trajectories. The plot also em-
follow thetodemonstrations closely (first row, fourth column).
phasizes that even reproductions starting in the vicinity of the the humanoid robot iCub [7] in addition strict form the experiments
of stability, without respect to the demonstrations. of learning is thus a prerequisite for such systems. The
The estimate leads to very accurate reproductions and also
demonstrations are prone to divergence. The second column discussed in the previous section.shows Such
The robots are typically
reproductions
a good generalization are directly converging
capability. towards
The results the attrac- experimental setting is illustrated in Fig. 6. A human tutor
for SEDS
& AI
illustrates the results for networks trained with respect to Lq . designed to solve service tasks are in shown
tor. environments
This isindue the to where
the
fifth highaviolation
column of theAs
of the figure. demonstrations
mentioned, by
It is shown that this Lyapunov candidate introduces a very high flexibility is required. Robust SEDS adaptability
Lq close to the to
is subject by
start means
of the demonstrations.
constraints corresponding to Column a quadratic three of
strict form of stability, without respect to the demonstrations. of learning is thus a prerequisite
Fig. for 5 such
shows systems.
the
Lyapunov function Lq . The estimate resultsThe for L P is very similar to candidate
. This Lyapunov the
The reproductions are directly converging towards the attrac- experimental setting is illustratedresults isin data-dependent
Fig. for6.the A networks
human buttutor
still
applying too Llimited
q or LPtoascapture
Lyapunov the full
tor. This is due to the high violation of the demonstrations by candidate.
structure The of the thirdJ-2tabular in Tab. I shows
demonstrations. Thethe for Fig.
resultscolumn
fourth of 6.the right
from
Kinesthetic teaching of iCub. The tutor moves iCub’s right arm
to the left side of the small colored tower.
the
the whole
figuredata set. Thethe
illustrates method using LELM
performance hasnetworks
of the the lowesttrained
Lq close to the start of the demonstrations. Column three of
trajectory
by LELMerror . Thevalues Lyapunovwhich iscandidate
due to the is high flexibility
strongly of physically
curved to guides iCubs right arm in the sense of kinesthetic
Fig. 5 shows the results for LP . This Lyapunov candidate the candidate function. SEDSclosely performs in the range of column).
the teaching using a recently established force control on the
is data-dependent but still too limited to capture the full follow the demonstrations (first row, fourth
quadratic functions due the conservative stability constraints. robot. The tutor can thereby actively move all joints of
structure of the J-2 demonstrations. The fourth column of The estimate leads to very accurate reproductions and also
However, SEDS has the appealing feature that the stability the arm to place the end-effector at the desired position.
the figure illustrates the performance of the networks trained isshows a goodby
guaranteed generalization
construction capability.
of the model Thewhereas
results fortheSEDS Beginning on the right side of the workspace, the tutor first
by LELM . The Lyapunov candidate is strongly curved to are shown in the fifth column of the figure. As mentioned,
hard- und software architectures
follow the demonstrations closely (first row, fourth column). SEDS is subject to constraints corresponding to a quadratic
The estimate leads to very accurate reproductions and also Lyapunov function Lq . The estimate is very similar to the
shows a good generalization capability. The results for SEDS results for the networks applying Lq or LP as Lyapunov
Fig. 6. Kinesthetic teaching of iCub. The tutor moves iCub’s right arm
are shown in the fifth column of the figure. As mentioned, candidate. The third tabular in Tab. I shows the results for from the right to the left side of the small colored tower.
the whole data set. The method using LELM has the lowest
SEDS is subject to constraints corresponding to a quadratic
trajectory error values which is due to the high flexibility of physically guides iCubs right arm in the sense of kinesthetic
Lyapunov function Lq . The estimate is very similar to the
the candidate function. SEDS performs in the range of the teaching using a recently established force control on the
results for the networks applying Lq or LP as Lyapunov
Fig. 6. Kinesthetic teaching of iCub. The quadratic functions
tutor moves due arm
iCub’s right the conservative stability constraints. robot. The tutor can thereby actively move all joints of
candidate. The third tabular in Tab. I shows the results for
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar Philosophie
from the right to the left side of the smallHowever, SEDS has the appealing feature that the stability
colored tower.
the whole data set. The method using LELM has the lowest the arm to place the end-effector at the desired position.
is guaranteed by construction of the model whereas the Beginning on the right side of the workspace, the tutor first
trajectory error values which is due to the high flexibility of physically guides iCubs right arm in the sense of kinesthetic
the candidate function. SEDS performs in the range of the teaching using a recently established force control on the
quadratic functions due the conservative stability constraints. robot. The tutor can thereby actively move all joints of
However, SEDS has the appealing feature that the stability the arm to place the end-effector at the desired position.
is guaranteed by construction of the model whereas the Beginning on the right side of the workspace, the tutor first
Platzhalter für Bild, Bild auf Titelfolie hinter das Logo einsetzen KI-Hype
Was treibt die Veränderungen ?
▪ Robotik
▪ künstliche Intelligenz, Datenverarbeitung
▪ Vernetzung Robotik
Quelle: Universität Bielefeld
Künstliche
Intelligenz
Quelle: Facebook research
Vernetzung
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar Philosophie
Vernetzung
▪ keine technische Grenze !
▪ Internet ist mobil !
▪ > 23 Milliarden Dinge vernetzt
▪ z.B. 400 Produkte von Miele
▪ Wertschöpfung wandert in Service
▪ Wertschöpfung durch Software !
Chi n a
+
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar Philosophie
KI - immer schon breit definiert
AI Magazine Volume 27 Number 4 (2006) (© AAAI)
A Proposal for thekte von
p e
Dartmouth r e :Summer
a l l e A s
M e r k m a l e
c t u r e a l
Research
.. c o n j e
o d Project
e r a n d e
n non
e n f o r m
…
e n k ö n
Artificial
Lern teIntelligence
n l l i g e n z
b e n w e r d e
o n I
v August h r i e u n d
b e s
31, c 1955 i n e n
e n a u a s c h r v e d
g m i t M “ r e s e
l l e s o c h
John McCarthy, Marvin L. Minsky,… a z t n
K I : a s j e t i s t …
Nathaniel Rochester,
e n , w a n s ”
and Claude E. Shannon
D a t h u m
for
■ The 1956 Dartmouth summer research project on guage, form abstractions and concepts, solve
artificial intelligence was initiated 17.01.2019
by this August kinds ofJochen
| Berlin | ©2017-19 problems now reserved
Steil | Seminar for humans,
Philosophie
31, 1955 proposal, authored by John McCarthy, and improve themselves. We think that a sig-
Marvin Minsky, Nathaniel Rochester, and Claude nificant advance can be made in one or more
Shannon. The original typescript consisted of 17
of these problems if a carefully selected group
pages plus a title page. Copies of the typescript are
of scientists work on it together for a summer.
housed in the archives at Dartmouth College and
“7 Todsünden der KI Vorhersagen”
Rodney Brooks (in Technology Review, 2017):
1. Über- & Unterschätzen
2. Magie
3. Leistung vs. Kompetenz
4. Kofferwörter (Lernen, Intelligenz)
5. Exponentiell
6. Hollywood
7. Geschwindigkeit der Verbreitung
https://www.heise.de/tr/artikel/Essay-Die-sieben-Todsuenden-der-KI-Vorhersagen-4003150.html
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar Philosophie
Platzhalter für Bild, Bild auf Titelfolie hinter das Logo einsetzen Robotik und Roboterlernen
Robotik heute
▪ Lösungen für viele spezielle Probleme
Franka Emika: 19kg
▪ neue “kleine Roboter”: Drohnen,
http://www.franka.de
Staubsauger, Transportwagen
iRobot.com
▪ Greifen & Manipulieren:
je feinfühliger, desto schwieriger,
aber Fortschritte
▪ großer “Baukasten” an Methoden
FANUC M-2000: 8
www.fanuc.eu
▪ profitieren von künstlicher Intelligenz Magazine
▪ (aber: Rethink Robotics
insolvent)
Miele RX1
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar Philosophie
Robotik vs. Science Fiction Roboterethik
Sie sind stark, klug, selbstständig.
Und was wird aus uns?
▪ der humanoide Roboter Öffentliche Tagung | 24.11.2015
Karl Storz Besucher- und
Schulungszentrum Berlin
▪ “alter Traum”
A t l a s
z e i l e : o t er
h l a g r R o b
S c
▪ ständiges SF-Thema
n o i d e ! ”
H u m a c o u r s
“
▪ appelliert an unsere
n P a
1940-1950 r
Vorstellungen von ka n ceres
cologne center for
ethics, rights, economics, and social sciences
of health
uns selbst !
▪ schwierig:
Unterscheide SF
von Realität !
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboterethik
Apell an (soziale) Interaktion: 11
Sie sind stark, klug, selbstständig.
Und was wird aus uns?
Gedächtnis
Öffentliche Tagung | 24.11.2015
Karl Storz Besucher- und
Schulungszentrum Berlin Motivation
Vorlieben
Intention
Fähigkeiten
Kontext
Aufgabe
Assoziationen
ceres
cologne center for
Sprache
ethics, rights, economics, and social sciences
of health
…
Beware the Anthropomorphism !
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar Philosophie”Kofferwort” Lernen
British researchers found Belohnungslernen
that electrical stimulation
of the brain sped
up learning. überwachtes Lernen
Assoziation
Konditionierung
unüberwachtes Lernen
Exploration
Imitation
Soziales Lernen
Induktion & Deduktion
Konzeptlernen
http://abcnews.go.com/Health/electrical-stimulation-speed-learning-stroke-recovery/story?id=14570429
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar Philosophie
...Arbeitsdefinition Lernen
Erfahrung auf neue Situationen generalisieren
mehr als: Erfahrung speichern und reproduzieren
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieLernszenario
Lernaufgabe(n)
Ereignis
Wahrnehmung
Motorik
Subjekt Sprache
Weltwissen
…
Gehirn
kognitive
Fähigkeiten
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieLernszenario !15
Lernaufgabe(n) Kontext
Lernaufgabe(n)
Situation
Ereignis
Wahrnehmung
Subjekt Motorik
Ziele
Sprache
Weltwissen
Motivation
…
Verhaltens- Gehirn Gehirn
steuerung
Präferenzen
Metakognition: Reflektion, Handlungssteuerung
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieLernszenario !16
Lernaufgabe(n) Gesellschaft
Lernaufgabe(n) Familie,
Kontext Moral,
Lernaufgabe(n)
Ereig
Situation Ethik
Wahrnehmung
Subjekt Motorik
Sprache
ZieleWeltwissen
Werte
Motivation
…
Verhaltens-
Normen
steuerung
…
Präferenzen Gehirn Gehirn Gehirn
Verantwortung: “Ich kann auch anders !“
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboterlernen
• Maschinelles Lernen: Statistik
— Kontext: Datenwelt —
• Roboterlernen: Fähigkeiten
— Kontext: physikalische Welt —
• Roboterlernen: soziale Fähigkeiten?
— Kontext: soziale Welt —
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieMaschinelles Lernen !18
(Big) Data
Lernaufgabe(n)
Ereignis
Spracherkennung
Wahrnehmung
Gesichtserkennung
Motorik
Software
Subjekt Ontologien
Sprache
Vorhersagen
Agent Neuronale Netze,
Weltwissen
… “Deep learning”
… S1
C1
S2
C2
Gehirn
Input
R
G High−dimensional C2 Feature Space
B Object Memory
Figure 3: Hierarchical object representation and object memory. Based on a
ROI with additional segmentation mask, the input is processed in a sequence of
topographically organized feature detection (S1,S2) and pooling stages (C1,C2).
The object memory provides an exemplar-based representation of views embed-
ded in the high-dimensional C2-feature space.
von Programmierung per Design vorgegeben
to avoid the occurence of spurious edges at wrong segmentation borders. In a
second step, a soft Winner-Takes-Most (WTM) mechanism is performed with
ql (x,y)
!
0 if 1 M < γ1 or M = 0,
r1l (x, y) = ql (x,y)−Mγ1 (2)
1
1−γ1 else,
where M = maxk q1k (x, y) and r1l (x, y) is the response after the WTM mech-
anism which suppresses sub-maximal responses. The parameter 0 < γ1 < 1
controls the strength of the competition. The activity is then passed through a
simple threshold function with a common threshold θ1 for all cells in layer S1:
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar Philosophie
hl1 (x, y) = H r1l (x, y) − θ1 ,
" #
(3)
where H(x) = 1 if x ≥ 0 and H(x) = 0 else and hl1 (x, y) is the final activity of
the neuron sensitive to feature l at position (x, y) in the S1 layer. The activities
of the first layer of pooling C1-cells are given byKI: Bsp Deep Face Gesichtserkennung
DeepFace: “Closing the Gap to Human-Level Performance in Face Verification”,
Facebook AI Research, CVPR, 2014
▪ “klassische Bildverarbeitung” notwendig
▪ besser als Menschen auf trainierten Gesichtern
▪ erkennt aber nichts, außer den trainierten Gesichtern
▪ (noch) hohe Kosten für Konfiguration & Training
▪ Daten allein (Bilder) beantworten keine Fragen !
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieLearning Hand-Eye Coordination for Robotic Grasping with Deep Learning
and Large-Scale Data Collection
We describe a learning-based approach to hand-
eye coordination for robotic grasping from
Robotik:
monocular End-to-End
images. To learn hand-eye Deep Reinforcement Learning
coordi- 20
nation for grasping, weLevine
Sergey trained a large convo- SLEVINE @ GOOGLE . COM
Peter Pastor
lutional neural network to predict the probabil- PETERPASTOR @ GOOGLE . COM
Learning Hand-Eye
ity that task-space
Alex Krizhevsky
motion of
Deirdre Quillen
Coordination
the gripper will re- for Robotic Grasping with Deep Learning
AKRIZHEVSKY @ GOOGLE . COM
DEQUILLEN @ GOOGLE . COM
grasps, usingand
sult in successful Google only Large-Scale
monocular Data Collection
rXiv:1603.02199v2 [cs.LG] 24 Mar 2016
camera images and independently of camera cal-
ibration or the current robot pose. This requires
the network to observe the spatial Abstract relationship
between
Sergey the gripper We
Levine anddescribe
objects in the scene,
a learning-based approach to hand- SLEVINE @ GOOGLE . COM
eye coordination for robotic grasping from
thusPastor
Peter learning hand-eye coordination. We then
monocular images. To learn hand-eye coordi-
PETERPASTOR @ GOOGLE . COM
Alex
useKrizhevsky
this network to servo
nationtheforgripper
grasping,inwereal timea large convo-
trained AKRIZHEVSKY @ GOOGLE . COM
Deirdre Quillen
to achieve successful lutional
grasps.neural Tonetwork
train our net- the probabil-
to predict DEQUILLEN @ GOOGLE . COM
ity that task-space motion of the gripper will re-
work, we collected over
Google 800,000 grasp attempts
sult in successful grasps, using only monocular
over the course of twocamera
months, imagesusing between 6of camera cal-
and independently
and 14 robotic manipulators
ibration orat theany given
current robot time,
pose. This requires
the network to observe the spatial relationship
with differences in Abstract
camera placement and hard-
between the gripper and objects in the scene,
ware. Our experimental thusevaluation demonstrates
learning hand-eye coordination. We then
We describe a learning-based
that our method achieves approach
effective
use this network real-timeto hand-
to servo the con- in real time
gripper
eye coordination for achieve
roboticsuccessful
grasping from
trol, can successfullyto grasp novel grasps.
objects, To train our net-
and
monocular images. To work,learn hand-eye
we collected over coordi-
800,000 grasp attempts
corrects mistakes by continuous
over the course servoing.
of two months, using between 6Figure 1. Our large-scale data collection setup, consisting
nation for grasping, we trained a large convo-
and 14 robotic manipulators at any given time,robotic manipulators. We collected over 800,000 grasp att
lutional neural network withtodifferences
predict in thecamera
probabil-placement and hard-
ity that task-space motion ware. Our of the gripper will
experimental re- demonstratesto train the CNN grasp prediction model.
evaluation
1. Introduction from Levine et al, 2016
sult in successful grasps, that our using
method only monocular
achieves effective real-time con-
trol, can successfully
camera images and independently of camera grasp cal-novel objects, anda feedback controller is exceedingly challenging.
When humans and animals engage
corrects in object
mistakes manipulation
by continuous servoing. Figure 1. Our large-scale data collection setup, consisting of 14
ibration or the current
17.01.2019robot pose.Jochen
| Berlin | ©2017-19 This requires
Steil | Seminar Philosophie
niques such as visual servoing (Siciliano & Khatib, 2
behaviors, the interaction inherently
the network to observe the spatial relationship involves a fast feed- robotic manipulators. We collected over 800,000 grasp attempts
performtocontinuous feedback
train the CNN grasp prediction on visual features, but
model.Robotik: End-to-End Deep Reinforcement Learning 21
Random Bin Picking – Approach
heute einfach !
State
r o b o t
o - e n d [Levine et al. 2016]
e n d - t r n i n g
c h e s n t l e a
t i s t i s c e m e b e l
… s t a i n f o r k t i k a
i n g / r e m p r a
le a r n h k a u )
k t i s c G o , …
pra Action Schac
h , „Reward“
o h l i n
e h r w
! b
data=teuer(a e r s Stochastic search
(Sampling), e.g. CMA-ES
© Dr. Felix Reinhart Machine Learning in Robotics
schwierig ! 17
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboterlernen
• Maschinelles Lernen: Statistik
— Kontext: Datenwelt —
• Roboterlernen: Fähigkeiten
— Kontext: physikalische Welt —
• Roboterlernen: soziale Fähigkeiten?
— Kontext: soziale Welt —
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboterlernen: Fähigkeiten
Lernaufgabe(n) (Small) Data
Spracherkennung
Gesichtserkennung
Ontologien
Vorhersagen
Roboter … Lernverfahren
Objekt nehmen
Regelung
Sensorik
Echtzeit
Sicherheit
Energie
Kräfte
Bewegung
…
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboterlernen: Fähigkeiten
Interactive Imitation Learning of Object Movement Skills,
M. Mühlig, J. Steil, M. Gienger, Autonomous Robots, 2012
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboterlernen: Fähigkeiten
Lernaufgabe(n) Vormachen
Lernverfahren
Roboter
Regelung
Sensorik “Objekte
Echtzeit Objekte stapeln
präparieren”
Sicherheit “ Interaktions-
Energie design”
Kräfte “neu starten”
Bewegung …
…
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboterlernen: Fähigkeiten
Perception & Learning
per Design vorgegeben
Movement Generation
Sequence
!26
Sequence
Perception & Learning
Procedural Memory
Selection Movement Generation
Movement Sequencing
Sequence
Sequence
Selection
Procedural Memory Movement Sequencing
Prediction and Planning
Interaction
Labeling
Prediction and Planning
Movement Primitive Memory Interaction Movement Primitives
Labeling Attention
System
Primitive
Movement Primitive Memory Movement Primitives GMR
Attention
System
Primitive
GMR
Scene
Interpretation
Optimization
Scene
Movement
Movement Learning
Interpretation
Optimization
Observation Memory
Movement
Movement Learning
Lernen
Observation Memory Posture
Recognition
Posture
Recognition
Attractor Command
Attractor Command
Body Scheme
Segmentation
BodyAdaptation
Scheme
Segmentation
Persistent Object Memory Adaptation
Assign Linked Motion Control
Persistent Object Memory Objects
Assign Linked Motion Control
Reactive
Objects
Reactive
Tutor Model
Tutor Model
t
t
Short Term
Object Filter ShortMemory
Term
Object Filter Memory
Perception
Perception MotorMotor Command
Command
Environment
Environment / Simulation
/ Simulation
Interactive Imitation Learning of Object Movement Skills,
M. Mühlig, J. Steil, M. Gienger, Autonomous Robots, 2012
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboterlernen
• Maschinelles Lernen: Statistik
— Kontext: Datenwelt —
• Roboterlernen: Fähigkeiten
— Kontext: physikalische Welt —
• Roboterlernen: soziale Fähigkeiten?
— Kontext: soziale Welt —
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboterlernen: soziale Fähigkeiten ?!
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboterlernen: soziale Fähigkeiten ?!
Zur Zeit kein umfassender Ansatz …
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboterlernen: soziale Fähigkeiten ?!
Lernaufgabe(n) Interaktion
Lernaufgabe(n)
Wahrnehmung Lernverfahren
Motorik
Sprache
Roboter Weltwissen
Ziele
…
Regelung Motivation Aufmerksamkeit
Turn-Taking
Sensorik Verhaltens- Sprachverstehen
“Namen sagen”
Echtzeit steuerung Multi-Personen Tracking
Situationsgedächtnis
Sicherheit Präferenzen Dialog
Umgang mit Ambiguität
Energie Umgang mit Unsicherheit
Umgang mit …
Kräfte Gedächtnis
Interne Simulation
Bewegung Situationsverstehen
… …
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophiePlatzhalter für Bild, Bild auf Titelfolie hinter das Logo einsetzen Robotik und Roboterlernen
Roboter als gefährliche Technologie
This open letter was announced July 28 at the opening of the IJCAI 2015 conference on July 28.
Journalists who wish to see the press release may contact Toby Walsh
[mailto:toby.walsh@nicta.com.au] .
Hosting, signature verification and list management are supported by FLI; for administrative
questions about this letter, please contact tegmark@mit.edu [mailto:tegmark@mit.edu] .
AUTONOMOUS WEAPONS: AN
OPEN LETTER FROM AI &
ROBOTICS RESEARCHERS
12.11.18: 3978 AI/Robotics Researcher Signatories
Autonomous weapons select and engage targets without human intervention. They might include,
12.11.18: 22540 Other Endorsers (S. Hawkings et al.)
of 772
Welche Anwendung wollen wir ?
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboter sind unterschätzt
• Roboter sind mächtige Werkzeuge
- zunehmend low-cost
- zunehmend leicht
- zunehmend variabel
- zunehmend billige Sensorik
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieInternational Journal of Robotics Research
Beispiel: Assistenz in Medizin
rg et al.
(a) Circle Cutting (b) Needle Passing (c) Suturing
11. Pull
7. Handoff 3. Handoff
9.Handoff
3. 1/2 cut 10. Insert
6. Pass 3 2.Pass 1 8. Pull
6. Finish
6.Handoff 7. Insert
2. Notch
5. Pull
4. Re-enter 4. Pass 2
5. 1/2 Cut 3.Handoff 4. Insert
1. Start
1.Start
8. Pass 4 2. Pull
5. Handoff 1. Insert
nd annotations of the three tasks: (a) circle cutting, (b) needle passing, and (c) suturing. Right arm actio
d leftTransition State
arm actions are listedClustering:
in yellow. Unsupervised
Surgical Trajectory Segmentation For Robot Learning
Robotik
Sanjay Krishnan*1, Animesh Garg*1, Sachin Patil1, Colin Lea2,
ng: A 5 cm diameter circle drawn on a piece (ignoring
Gregory Hager2, Pieter Abbeel1, Ken Goldberg1
the orientation). In particular, we sho
e first step is to cut a notch into the circle. (red box) to highlight the benefits of these featu
(vergl.
tep is auch
to cut Keynote Jeffrey
clockwise half-way Hager, Int. Conf.
around the Intelligent Robots, phase
the cross-over 2015, of theKünstliche
task, the robot has to
Intelligenz
US patent application on skill evaluation)
the robot transitions to the other side cutting notch point and adjust to cut the other half of the c
wise. Finally, the robot finishes the cut at the only using the end-effector kinematic pose, th
Vernetzung
Daten,
Netzwerk
of the two cuts. As17.01.2019
the left| Berlin |arm’s only
©2017-19 Jochen Steil | action where this transition happens is unreliable as op
Seminar Philosophie
n the gauze in tension, we exclude it from approach the entry from slightly different ang
F
In Figure 10a, we mark 6 manually identified other hand, the use of a gripper contact binary feaRoboter & Big Data
Beispiel: Assistenz in Medizin
DaVinci:
• in großer Zahl vorhanden Da-Vinci Operationsroboter
• zeichnet Operationen auf
• Big-Data Methoden zur Bewertung
=> Liefert quantifizierbaren Maßstab zur Bewertung
der manuellen Fähigkeit der Chirurgen
(vgl. Keynote Jeffrey Hager, Int. Conf. Intelligent Robots, Hamburg, 2015)
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboter & Big Data
Beispiel: Assistenz in Medizin
Da-Vinci Operationsroboter
(ethische) Fragen:
• Bezahlung von Operationen nach Qualität ?
• Welche “Abweichungen” sind tolerabel ?
• Wie soll das fehlerbehaftete (!)
Lernen der Ärzte organisiert sein?
Wollen wir alles vermessen ?
Wieviel Datengläubigkeit ist sinnvoll ?
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboter, Lernen & Personalisierung
Vermessung von Interaktion
Beispiel: Assistenz in Produktion
Was lernen solche Roboter über Menschen ?
Interaktionsdaten sind wie Gesundheitsdaten !
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieRoboter, Lernen & Personalisierung
Vermessung von Interaktion
Beispiel: Assistenz in Produktion
(ethische) Fragen:
• Auswertung für Gesundheitsüberwachung ?
• Gerechtigkeitsfragen
— bekommt jeder gleiche Assistenz ?
Welche Daten wollen wir wie nutzen ?
Assistenz und Überwachung sind janusköpfig.
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophiePlatzhalter für Bild, Bild auf Titelfolie hinter das Logo einsetzen Fragen & Überlegungen
Einordnung Roboterlernen
• Kombination Lernen und Anthropomorphismus
führt leicht in die Irre
• Maschinelles Lernen und Roboterlernen sind
sehr erfolgreich für einzelne Fähigkeiten
• Komplexität von Metakognition & Lernen auf der
Systemebene ist aber zu hoch
Fazit:
— Roboter als “verantwortliche Person” bleibt Fiktion
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieEhtik in der Anwendung
• (speziellere !) Roboter sind mächtige Technologie
• Roboter “erben” alle Probleme von Big Data
• Roboterlernen verschärft diese Datenprobleme
• Assistenz & Überwachung: 2 Seiten einer Medaille !
Welche Anwendung wollen wir ?
Wollen wir alles vermessen ?
Welche Daten wollen wir wie nutzen ?
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieMythen
e h l e n
▪ Deutschland : e s f &
c h e n
▪ verpasst seine Zukunft fals tiker/inn
o r m a t u r !
▪ hat nicht genügend Experten Inf r a s t r u k
▪ hat keine gute Forschung In f
▪ …
sch …
agi
▪ die Singularität, KI übernimmt alles .. m
……….
n e K I
▪ Selbst-Lernen : k e i ne
c h o h
fals ahmen,
e An n n g e n
▪ der KI ist inhärent, selbst-… ohn h e i d u
Ents c
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieArbeit & Ethik
▪ Prognose: mehr hybride Mensch-Maschine Interaktion
▪ Aber: meist wird viel zu aufgeregt diskutiert
▪ es sind keine Maschinenwesen mit Moral, Ethik in Sicht
▪ Und:
ist die falsche Diskussion
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar Philosophienow: Brook’s 1. Fallacy
Roboterlernen wird wegen
Vermenschlichung überschätzt ! f ü r
D a n k k
i e l e n r k s a m
Aber gleichzeitig: V f m e
r e A u
ih
Die Kombination Roboter & Lernen
wird in Anwendungen unterschätzt!
17.01.2019 | Berlin | ©2017-19 Jochen Steil | Seminar PhilosophieYou can also read
