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AI and Robotics: LessonsLearned from the RACEProject
Federico Pecora?
Center for Applied AutonomousSensor Systems (AASS)
Örebro University, Sweden © Joshua Ellingson
? Contributors:
A. Saffiotti, T. Cohn, K. Dubba, J. Hertzberg,L. Hotz, Š. Konecný, J. Lehamnn,L. Lopes, M. Mansouri, B. Neumann,M. de Oliveira, S. Rockel, S. Stock, L. Zhang
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
AI + Robotics — Why?
Making robots intelligent has beena common goal of AI andRobotics since Shakey
“The general purpose robot is amirage”[Debate on failure of AI, BBC“Controversy” series, 1973]
But what does AI contribute toRobotics? And vice-versa?
© 2013 F. Pecora / Örebro University – aass.oru.se 2 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
AI + Robotics — Why?
Making robots intelligent has beena common goal of AI andRobotics since Shakey
“The general purpose robot is amirage”[Debate on failure of AI, BBC“Controversy” series, 1973]
But what does AI contribute toRobotics? And vice-versa?
© 2013 F. Pecora / Örebro University – aass.oru.se 2 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Model-Centered Approach
“Robotics is closely related to AI [as] intelligence isrequired for manipulation, navigation, localization,mapping, motion planning”[Wikipedia page on Artificial Intelligence]
Model-centered approach is a major contribution of AI toRoboticsModels capture environment, capabilities, tasks
“competent behavior” results from reasoningmodels have formal propertiesmodels can be changed to suit different environments,physical capabilities, tasks
Which models are useful for autonomous robots?
© 2013 F. Pecora / Örebro University – aass.oru.se 3 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Model-Centered Approach
“Robotics is closely related to AI [as] intelligence isrequired for manipulation, navigation, localization,mapping, motion planning”[Wikipedia page on Artificial Intelligence]
Model-centered approach is a major contribution of AI toRoboticsModels capture environment, capabilities, tasks
“competent behavior” results from reasoningmodels have formal propertiesmodels can be changed to suit different environments,physical capabilities, tasks
Which models are useful for autonomous robots?
© 2013 F. Pecora / Örebro University – aass.oru.se 3 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Model-Centered Approach
“Robotics is closely related to AI [as] intelligence isrequired for manipulation, navigation, localization,mapping, motion planning”[Wikipedia page on Artificial Intelligence]
Model-centered approach is a major contribution of AI toRoboticsModels capture environment, capabilities, tasks
“competent behavior” results from reasoningmodels have formal propertiesmodels can be changed to suit different environments,physical capabilities, tasks
Which models are useful for autonomous robots?
© 2013 F. Pecora / Örebro University – aass.oru.se 3 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Scenario: a Busy Restaurant
knife
fork
dish
time
Spatial models: cappuccinosshould be served “in front of”guests
Resource models: cappuccinoswon’t fit if other dishes arepresentAction models: dishes can becleared to make space forcappuccinosOntological models: dishes andcutlery should be cleared, saltand pepper should stayTemporal models: cappuccinosshould be served before they getcold
© 2013 F. Pecora / Örebro University – aass.oru.se 4 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Scenario: a Busy Restaurant
knife
fork
dish
time
Spatial models: cappuccinosshould be served “in front of”guestsResource models: cappuccinoswon’t fit if other dishes arepresent
Action models: dishes can becleared to make space forcappuccinosOntological models: dishes andcutlery should be cleared, saltand pepper should stayTemporal models: cappuccinosshould be served before they getcold
© 2013 F. Pecora / Örebro University – aass.oru.se 4 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Scenario: a Busy Restaurant
knife
fork
dish
time
Spatial models: cappuccinosshould be served “in front of”guestsResource models: cappuccinoswon’t fit if other dishes arepresentAction models: dishes can becleared to make space forcappuccinos
Ontological models: dishes andcutlery should be cleared, saltand pepper should stayTemporal models: cappuccinosshould be served before they getcold
© 2013 F. Pecora / Örebro University – aass.oru.se 4 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Scenario: a Busy Restaurant
knife
fork
dish
time
Spatial models: cappuccinosshould be served “in front of”guestsResource models: cappuccinoswon’t fit if other dishes arepresentAction models: dishes can becleared to make space forcappuccinosOntological models: dishes andcutlery should be cleared, saltand pepper should stay
Temporal models: cappuccinosshould be served before they getcold
© 2013 F. Pecora / Örebro University – aass.oru.se 4 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Scenario: a Busy Restaurant
knife
fork
dish
time
Spatial models: cappuccinosshould be served “in front of”guestsResource models: cappuccinoswon’t fit if other dishes arepresentAction models: dishes can becleared to make space forcappuccinosOntological models: dishes andcutlery should be cleared, saltand pepper should stayTemporal models: cappuccinosshould be served before they getcold
© 2013 F. Pecora / Örebro University – aass.oru.se 4 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Scenario: a Busy Restaurant
knife
fork
dish
time
Action+Resource models: traycapacity determines number oftrips to clear a table
Ontological+Spatial models: typeof meal affects spatial layout. . .
© 2013 F. Pecora / Örebro University – aass.oru.se 5 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Scenario: a Busy Restaurant
knife
fork
dish
time
Action+Resource models: traycapacity determines number oftrips to clear a tableOntological+Spatial models: typeof meal affects spatial layout. . .
© 2013 F. Pecora / Örebro University – aass.oru.se 5 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Key Challenges
? What modeling languages areappropriate?
How do we jointly reason aboutdifferent models?How do we learn relevant modelsfrom few examples?How to we obtain symbolic models(appropriate for reasoning) fromexperience?
© 2013 F. Pecora / Örebro University – aass.oru.se 6 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Key Challenges
? What modeling languages areappropriate?How do we jointly reason aboutdifferent models?
How do we learn relevant modelsfrom few examples?How to we obtain symbolic models(appropriate for reasoning) fromexperience?
© 2013 F. Pecora / Örebro University – aass.oru.se 6 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Key Challenges
? What modeling languages areappropriate?How do we jointly reason aboutdifferent models?How do we learn relevant modelsfrom few examples?
How to we obtain symbolic models(appropriate for reasoning) fromexperience?
© 2013 F. Pecora / Örebro University – aass.oru.se 6 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Key Challenges
? What modeling languages areappropriate?How do we jointly reason aboutdifferent models?How do we learn relevant modelsfrom few examples?How to we obtain symbolic models(appropriate for reasoning) fromexperience?
© 2013 F. Pecora / Örebro University – aass.oru.se 6 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Robustness by Autonomous Competence Enhancement
Goal of RACE
Enable robots to operate more robustly by exploiting experiences
Methodology in RACE
Experiences are transformed into sub-symbolic andsymbolic models
Hand-coded and learned models are refined over time
Hand-coded and learned models are used for reasoning
© 2013 F. Pecora / Örebro University – aass.oru.se 7 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Robustness by Autonomous Competence Enhancement
Goal of RACE
Enable robots to operate more robustly by exploiting experiences
Methodology in RACE
Experiences are transformed into sub-symbolic andsymbolic models
Hand-coded and learned models are refined over time
Hand-coded and learned models are used for reasoning
© 2013 F. Pecora / Örebro University – aass.oru.se 7 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
RACE Partners
University of Hamburg (DE)Centre of Intelligent Systems and Robotics
University of Leeds (UK)School of Computing
Örebro University (SE)Center for Applied Autonomous SensorSystems
University of Osnabrück (DE)Institite for Computer Science
University of Aveiro (PT)Inst. for Electronics and TelematicsEngineering
HITeC (DE)Hamburger Informatik Technologie-Center,eV
EU-FP7 STREP, Objective 2.1: CognitiveSystems and Robotics, 2011–2015Funding: 2,997,298 e, 3 years
© 2013 F. Pecora / Örebro University – aass.oru.se 8 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
The Metaphor of Description Logics[S. Rockel et al., “An Ontology-based Multi-level Robot Architecture for Learning from Experiences”]
T-Box: the world’s rules — “general knowledge”
A-Box: individuals and their properties — “currentknowledge”
© 2013 F. Pecora / Örebro University – aass.oru.se 9 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
The Metaphor of Description Logics[S. Rockel et al., “An Ontology-based Multi-level Robot Architecture for Learning from Experiences”]
T-Box: the world’s rules — “general knowledge”
A-Box: individuals and their properties — “currentknowledge”
© 2013 F. Pecora / Örebro University – aass.oru.se 9 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
The Metaphor of Description Logics[S. Rockel et al., “An Ontology-based Multi-level Robot Architecture for Learning from Experiences”]
T-Box: the world’s rules — “general knowledge”
A-Box: individuals and their properties — “currentknowledge”
© 2013 F. Pecora / Örebro University – aass.oru.se 9 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
“Mental State” as a Collection of Fluents
end−boundsstart−bounds
fluent
is−a
Concept
has−ahas−a
[ls , us ] [le , ue ]
!FluentClass_Instance: [On, on1]Starttime: [10, 10]FinishTime: [11, ?]Properties:
[hasPhysicalEntity, PhysicalEntity, mug1][hasArea, Area, placingAreaWestRightTable1]
© 2013 F. Pecora / Örebro University – aass.oru.se 10 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
“Mental State” as a Collection of Fluents
end−boundsstart−bounds
fluent
is−a
Concept
has−ahas−a
[ls , us ] [le , ue ]
!FluentClass_Instance: [PlacingAreaWestRight,
placingAreaWestRightTable1]StartTime: [0, 0]FinishTime: [INF, INF]Properties:
[hasManipulationArea, ManipulationArea,manipulationAreaSouthTable1]
© 2013 F. Pecora / Örebro University – aass.oru.se 10 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
“Mental State” as a Collection of Fluents
end−boundsstart−bounds
fluent
is−a
Concept
has−ahas−a
[ls , us ] [le , ue ]
!FluentClass_Instance: [Mug, mug1]StartTime: [0, 0]FinishTime: [INF, INF]Properties:
[hasBoundingBox, BoundingBox, boundingBoxMug1][hasPose, Pose, poseMug1]
© 2013 F. Pecora / Örebro University – aass.oru.se 10 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Fluents as the Mental State of the Robot
© 2013 F. Pecora / Örebro University – aass.oru.se 11 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Leveraging Ontological Models
Expressing the robot’s knowledge in OWL-2-DL providessome useful services
consistency of robot’s knowledgescene interpretationlearning from few examples
© 2013 F. Pecora / Örebro University – aass.oru.se 12 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Generalizing from Positive Examples
Autonomous concept creation for ServeCoffee activity from a singleexample
guest1
mug1
table1 table2
robot
counter1
“Move to counter1, grasp mug1, move to south of table1, place mug1 atplacement area west — this is a ServeCoffee”
© 2013 F. Pecora / Örebro University – aass.oru.se 13 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Generalizing from Positive Examples
Generalization of ServeCoffee concept to cover current and previousexamples
mug1
guest2 table2
robot
counter1
table1
“Move to counter1, grasp mug2, move to north of table1, place mug2 atplacement area east — this is also a ServeCoffee”
© 2013 F. Pecora / Örebro University – aass.oru.se 13 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Generalizing from Positive Examples
Further generalization of ServeCoffee concept and application to a newsituation
mug1
table1
guest3
robot
table2
counter1
“Do a ServeCoffee to guest3 at table2”
© 2013 F. Pecora / Örebro University – aass.oru.se 13 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Conceptualization, Generalization and Unseen States[L. Hotz, B. Neumann, S. von Riegen, N. Worch. “Using Ontology-based Experiences for Supporting Robots Tasks”]
1 Conceptualize ServeCoffeeA
2 Conceptualize ServeCoffeeB
3 MLCS(ServeCoffeeA ,ServeCoffeeB )⇒ ServeCoffeeAB
4 MCSh (episode, initialStateC ) =ServeCoffeeAB
5 MLCS(ServeCoffeeAB ,initialStateC )⇒ ServeCoffeeABC
© 2013 F. Pecora / Örebro University – aass.oru.se 14 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Using the Most Appropriate KR&R Approach
Not all reasoning tasks fall within the scope of OWL-2-DLnot all types of knowledge are easy to represent inOWL-2-DLnot all reasoning tasks are amenable to DL inference
Reified constraints
Class: SceneLayoutEquivalentTo: Occurrence
AND (hasPassiveObject SOME PassiveObject)AND (hasLayoutConstraint EXACTLY 1 LayoutConstraint)
Class: TableLayoutEquivalentTo: SceneLayout ...
© 2013 F. Pecora / Örebro University – aass.oru.se 15 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Spatial Knowledge: Qualitative & Metric Constraints
A BB
A
B
A
A B
A
B
A
B
A B A B
y
B−x B+x A−x A+
x
B−y
B+y
B
A
A+y
A−y
x
N
W
A PO B A TPP−1 B A NTPP−1 BA EQ B
A DC B A EC B A TPP B A NTPP B
NW NE
E
SESSW
〈Before, Before〉
DC
Representation: Region Connection Calculus, CardinalDirection Calculus, (Augmented) Rectangle Algebra, ARA+
Reasoning: qualitative and metric constraint propagation
© 2013 F. Pecora / Örebro University – aass.oru.se 16 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Temporal Knowledge: Qualitative & Metric Constraints
Representation: (Augmented) Allen’s Interval Algebra,(disjunctive) temporal constraints
Reasoning: search (TCSP) and 3-consistency (STP)
© 2013 F. Pecora / Örebro University – aass.oru.se 17 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Resource Knowledge: Multi-Capacity Reusable Resources
timetime
reso
urc
e usa
ge
max capacity
Representation: can model constraints like max weight thatthe robot can carry
Reasoning: precedence constraint posting approach
© 2013 F. Pecora / Örebro University – aass.oru.se 18 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Building Blocks for Solving “Pixels to Predicates” ExistLearning/recognizing spatio-temporal
relations from video
Allen’s temporalrelations
Spatial relations
Objects
before meets
P
meets
PO DR
[M. Sridhar, A.G. Cohn, D.C. Hogg, 2011. “Benchmarkingqualitative spatial calculi for video activity analysis”]
Learning object categories (objectrecognition, tracking, anchoring)
[A. Chauhan, Z. Lu, L. Seabra Lopes, 2011.“Manhattan-Pyramid Distance: A Solution to an Anomaly
in Pyramid Matching by Minimization”]
© 2013 F. Pecora / Örebro University – aass.oru.se 19 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Reasoning with Diverse Knowledge[M. Mansouri, F. Pecora, 2013. “A Representation for Spatial Reasoning in Robotic Planning”]
Goal: place cup1 on table2 so that the table is well set
This requires hybrid reasoning
causal reasoning (planning)
temporal reasoning
spatial reasoning
ontological reasoning
resource reasoning
© 2013 F. Pecora / Örebro University – aass.oru.se 20 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Spatio-Temporal-Resource Planning[M. Mansouri, F. Pecora, 2014. “Planning with Space, Time and Resources for Robots”]
© 2013 F. Pecora / Örebro University – aass.oru.se 21 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Hybrid Search Space
...
...
...
time
1: pick(fork1,table1)
1: place(fork1,tray1)
2: pick(knife3,table1)
3: place(knife3,table1)
4: pick(fork1,tray1)
5: place(fork1,table1)
max capacity
time
nu
mb
ero
f ar
ms
Sol
utio
nsto
caus
alsu
b-pr
oble
m
Solut
ions to
resou
rcesu
b-pr
oblem
Solutions to temporal sub-problem
© 2013 F. Pecora / Örebro University – aass.oru.se 22 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Hybrid Search SpaceS
olut
ions
toca
usal
sub-
prob
lem
Solut
ions to
resou
rcesu
b-pr
oblem
Solutions to temporal sub-problem
© 2013 F. Pecora / Örebro University – aass.oru.se 22 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Hybrid Search SpaceS
olut
ions
toca
usal
sub-
prob
lem
Solutions to temporal sub-problem
Solut
ions to
resou
rcesu
b-pr
oblem
How toexplorethis searchspace?
© 2013 F. Pecora / Örebro University – aass.oru.se 22 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Towards a General Hybrid Reasoning Schema
Meta−CSP
Meta−values
Meta−variables
Ground−CSP
Ground−values
Ground−variables
Meta−constraints
Ground−constraintsd ⊆ D
δ ⊆ LD
(expressed in LD )
High-level decisions High-level requirements
Low-level decisions Low-level requirements
Sources and binaries onMaven Central and Google Code
metacsp.org
© 2013 F. Pecora / Örebro University – aass.oru.se 23 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Semantic Planning & Execution Monitoring
Can we use temporal/spatial/ontological models duringexecution?
Task: bring a mug from counter1 to table1
Initial condition (known to the planner): On(mug1, counter1)Plan (HTN):
1 go to counter12 grasp mug13 bring mug1 to table1
What could go wrong?
© 2013 F. Pecora / Örebro University – aass.oru.se 24 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Semantic Planning & Execution Monitoring
Can we use temporal/spatial/ontological models duringexecution?
Task: bring a mug from counter1 to table1
Initial condition (known to the planner): On(mug1, counter1)Plan (HTN):
1 go to counter12 grasp mug13 bring mug1 to table1
What could go wrong?
© 2013 F. Pecora / Örebro University – aass.oru.se 24 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Semantic Planning & Execution Monitoring
Can we use temporal/spatial/ontological models duringexecution?
Task: bring a mug from counter1 to table1
Initial condition (known to the planner): On(mug1, counter1)Plan (HTN):
1 go to counter12 grasp mug13 bring mug1 to table1
What could go wrong?
© 2013 F. Pecora / Örebro University – aass.oru.se 24 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Temporal/Ontological Execution Models[Š. Konecný et al., 2014. “Planning Domain & Execution Semantics: a Way Towards Robust Execution?”]
"object dropped
while grasping"
time
"object not grasped"
timetime
time
Observe(table1) Pick(mug1,table1)
holding(mug1)on(mug1,table1)
Move(counter,table1)
at(counter) at(table1)
holding(mug1)
Pick(mug1,table1)
Failure (contains)
Failure (before)
Pick(mug1,table1)
effect OWLprecondition
Pick(mug1,table1)
Success (meets ∨ overlaps)
{before}
{meets, overlaps}{meets, overlaps}
{meets, overlaps}
{meets, overlaps} {meets, overlaps, before}
holding(mug1)
Execution modelis-a
DrinkingVessel
Pick(cup22,table1)
cup22
Success (mug1 ↔ cup22)
holding(mug1)
holding(cup22)
© 2013 F. Pecora / Örebro University – aass.oru.se 25 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
Simulation[S. Rockel et al., 2014. “An Hyperreality Imagination based Reasoning and Evaluation System”]
Simulation as a tool for failure detection . . .
. . . and for use during planning?
© 2013 F. Pecora / Örebro University – aass.oru.se 26 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
What AI Can Do for Robots. . .
AI has studied how to reason within specific KR schemasfor decades
planning (STRIPS/PDDL, HTN, CBP, . . . )temporal reasoning (Allen’s Algebra, STP, TCSP, DTP, . . . )spatial reasoning (RCC, CDC, RA, ARA, . . . )scheduling (reusable resources, consumable resources,timetabling, . . . )logics (FOL, DL, . . . )
Reasoning can lead to competent behavior
Models can be adapted to changing conditions,requirements, tasks, goals
Models can facilitate robot programming
© 2013 F. Pecora / Örebro University – aass.oru.se 27 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
. . . and What Robots Can Do for AI
Each KR schema makes different restricitve assumptionsrobots will erode these restrictive assumptions
Few and naïve efforts in integrating different AI problemsolving techniques
robots will push integration of different branches of AI
AI researchers take perception for grantedrobots will require us to solve the pixels to predicatesquestion
AI problem solving is increasingly self-referentialrobots will finally provide a meaningful benchmark for AItechniques
© 2013 F. Pecora / Örebro University – aass.oru.se 28 / 29
How is AI Useful for Robots? Knowledge Representation Learning from Few Examples Planning & Execution Conclusions
ああありりりがががとととううう !
© 2013 F. Pecora / Örebro University – aass.oru.se 29 / 29