cognition-enabled robot control for mixed ... - icarus project · july 2014 cognition-enabled robot...
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Artificial Intelligence
Cognition-enabled Robot Control for MixedHuman-Robot Rescue Teams
Fereshta Yazdani,Benjamin Brieber and Michael Beetz
Institute for Artificial IntelligenceUniversitat Bremen
18. July 2014
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Artificial Intelligence
Simulation-based Rescue Scenario
F. Yazdani18. July 2014
Cognition-enabled Robot Control in a Human-Robot Team2
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Artificial Intelligence
Contents
• Upcoming Problems
• Cognition-enabled control Framework
– Interpretation of Plan– Interpretation of vague Instructions
• Framework in Use
• Conclusion & Future Work
F. Yazdani18. July 2014
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Artificial Intelligence
Motivation
“Go over there”
Upcoming Problems:
• plan execution with differentrobot control systems
• naturalistic task through vague,incomplete and ambigiousmultimodal instructions
• time! forcoordinating/organising therobots
F. Yazdani18. July 2014
Cognition-enabled Robot Control in a Human-Robot Team4
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Artificial Intelligence
Motivation
“Go over there”
Upcoming Problems:
• plan execution with differentrobot control systems
• naturalistic task through vague,incomplete and ambigiousmultimodal instructions
• time! forcoordinating/organising therobots
F. Yazdani18. July 2014
Cognition-enabled Robot Control in a Human-Robot Team5
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Artificial Intelligence
Motivation
“Go over there”
Upcoming Problems:
• plan execution with differentrobot control systems
• naturalistic task through vague,incomplete and ambigiousmultimodal instructions
• time! forcoordinating/organising therobots
F. Yazdani18. July 2014
Cognition-enabled Robot Control in a Human-Robot Team6
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Artificial Intelligence
Cognition-enabled Control System
“Go over there”
F. Yazdani18. July 2014
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Artificial Intelligence
Cognition-enabled Control Framework
“Go over there”
F. Yazdani18. July 2014
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Artificial Intelligence
Interpretation of Plan
High-level Plan “Take a picture”
( at− l o c a t i o n ( a− l o c a t i o n ( to−s e e a r t i f a c t ) )( p e r c e i v e a r t i f a c t ) )
( make−d e s i g l o c a t i o n ( ( to−s e e a r t i f a c t ) )( with−camera my−camera )( movement−t y p e d r i v i n g ) ) )
( make−d e s i g l o c a t i o n ( ( to−s e e a r t i f a c t ) )( with−camera my−camera )( movement−t y p e f l y i n g ) ) )
F. Yazdani18. July 2014
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Artificial Intelligence
Interpretation of Plan
High-level Plan “Take a picture”
( at− l o c a t i o n ( a− l o c a t i o n ( to−s e e a r t i f a c t ) )( p e r c e i v e a r t i f a c t ) )
( make−d e s i g l o c a t i o n ( ( to−s e e a r t i f a c t ) )( with−camera my−camera )( movement−t y p e d r i v i n g ) ) )
( make−d e s i g l o c a t i o n ( ( to−s e e a r t i f a c t ) )( with−camera my−camera )( movement−t y p e f l y i n g ) ) )
F. Yazdani18. July 2014
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Artificial Intelligence
Querying Capabilities of Robot Systems
• KnowRob(Tenorth et al., 2013) provides features for autonomousrobot control
• query for camera properties
:− c o m p o n e n t p r o p e r t i e s ( main camera , Prop , Value ) .Prop = imageSizeX ,Value = 6 4 0 ;Prop = imageSizeY ,Value = 8 0 0 ;. . .
• query for movement-base “flying”
:− c a p a v a i l a b l e o n r o b o t ( f l y i n g b a s e , s e l f ) .t r u e
F. Yazdani18. July 2014
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Artificial Intelligence
Querying Capabilities of Robot Systems
• KnowRob(Tenorth et al., 2013) provides features for autonomousrobot control
• query for camera properties
:− c o m p o n e n t p r o p e r t i e s ( main camera , Prop , Value ) .Prop = imageSizeX ,Value = 6 4 0 ;Prop = imageSizeY ,Value = 8 0 0 ;. . .
• query for movement-base “flying”
:− c a p a v a i l a b l e o n r o b o t ( f l y i n g b a s e , s e l f ) .t r u e
F. Yazdani18. July 2014
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Artificial Intelligence
Querying Capabilities of Robot Systems
• KnowRob(Tenorth et al., 2013) provides features for autonomousrobot control
• query for camera properties
:− c o m p o n e n t p r o p e r t i e s ( main camera , Prop , Value ) .Prop = imageSizeX ,Value = 6 4 0 ;Prop = imageSizeY ,Value = 8 0 0 ;. . .
• query for movement-base “flying”
:− c a p a v a i l a b l e o n r o b o t ( f l y i n g b a s e , s e l f ) .t r u e
F. Yazdani18. July 2014
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Artificial Intelligence
Querying Capabilities of Robot Systems
• KnowRob(Tenorth et al., 2013) provides features for autonomousrobot control
• query for camera properties
:− c o m p o n e n t p r o p e r t i e s ( main camera , Prop , Value ) .Prop = imageSizeX ,Value = 6 4 0 ;Prop = imageSizeY ,Value = 8 0 0 ;. . .
• query for movement-base “flying”
:− c a p a v a i l a b l e o n r o b o t ( f l y i n g b a s e , s e l f ) .t r u e
F. Yazdani18. July 2014
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Artificial Intelligence
Interpreting Multimodal Instructions
Interpretation from high-level instructions into low-level descriptions isvery difficult!
• symbolic descriptions called designators in CRAM(Moesenlechner etal., 2010)(Beetz et al., 2012)
• descriptions contain symbolic constraints to restrict solution space
(a location
(visible
(a location (position-of artifact)
(pointed-at
(a gesture (agent-team-leader))))))
F. Yazdani18. July 2014
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Artificial Intelligence
Interpreting Multimodal Instructions
Interpretation from high-level instructions into low-level descriptions isvery difficult!
• symbolic descriptions called designators in CRAM(Moesenlechner etal., 2010)(Beetz et al., 2012)
• descriptions contain symbolic constraints to restrict solution space
(a location
(visible
(a location (position-of artifact)
(pointed-at
(a gesture (agent-team-leader))))))
F. Yazdani18. July 2014
Cognition-enabled Robot Control in a Human-Robot Team16
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Artificial Intelligence
Interpreting Multimodal Instructions
Interpretation from high-level instructions into low-level descriptions isvery difficult!
• symbolic descriptions called designators in CRAM(Moesenlechner etal., 2010)(Beetz et al., 2012)
• descriptions contain symbolic constraints to restrict solution space
(a location
(visible
(a location (position-of artifact)
(pointed-at
(a gesture (agent-team-leader))))))
F. Yazdani18. July 2014
Cognition-enabled Robot Control in a Human-Robot Team17
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Artificial Intelligence
Effect-based Action Parameterization
• translating descriptions into action parameters for the robotnavigation routines
(<- (desig ?desig ?loc1)
(desig-prop ?desig visible))
(<- (desig ?desig visible ?loc2)
(desig-prop ?desig (position-of ?obj))
(desig-prop ?desig (pointed-at ?ldr)))
• generating sampling-based solutions with generative model approach!
F. Yazdani18. July 2014
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Artificial Intelligence
Effect-based Action Parameterization
• translating descriptions into action parameters for the robotnavigation routines
(<- (desig ?desig ?loc1)
(desig-prop ?desig visible))
(<- (desig ?desig visible ?loc2)
(desig-prop ?desig (position-of ?obj))
(desig-prop ?desig (pointed-at ?ldr)))
• generating sampling-based solutions with generative model approach!
F. Yazdani18. July 2014
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Artificial Intelligence
Effect-based Action Parameterization
• translating descriptions into action parameters for the robotnavigation routines
(<- (desig ?desig ?loc1)
(desig-prop ?desig visible))
(<- (desig ?desig visible ?loc2)
(desig-prop ?desig (position-of ?obj))
(desig-prop ?desig (pointed-at ?ldr)))
• generating sampling-based solutions with generative model approach!
F. Yazdani18. July 2014
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Artificial Intelligence
Effect-based Action Parameterization
setof ?Pose InAreaOf(pointedDirection, ?Pose) ?Poses ∧ member(?P,?Poses) ∧ Pose(CloseToSOF, ?P) ∧ beVisible(ForHumanLeader)
F. Yazdani18. July 2014
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Artificial Intelligence
Effect-based Action Parameterization
setof ?Pose InAreaOf(pointedDirection, ?Pose) ?Poses ∧ member(?P,?Poses) ∧ Pose(CloseToSOF, ?P) ∧ beVisible(ForHumanLeader)
1. setof ?PoseInAreaOf(pointedDirection, ?Pose)?Poses
2. member(?P, ?Poses)
3. Pose(CloseToSOF, ?P)
4. beVisible(ForHumanLeader)
F. Yazdani18. July 2014
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Artificial Intelligence
Effect-based Action Parameterization
setof ?Pose InAreaOf(pointedDirection, ?Pose) ?Poses ∧ member(?P,?Poses) ∧ Pose(CloseToSOF, ?P) ∧ beVisible(ForHumanLeader)
1. setof ?PoseInAreaOf(pointedDirection, ?Pose)?Poses
2. member(?P, ?Poses)
3. Pose(CloseToSOF, ?P)
4. beVisible(ForHumanLeader)
F. Yazdani18. July 2014
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Artificial Intelligence
Effect-based Action Parameterization
setof ?Pose InAreaOf(pointedDirection, ?Pose) ?Poses ∧ member(?P,?Poses) ∧ Pose(CloseToSOF, ?P) ∧ beVisible(ForHumanLeader)
1. setof ?PoseInAreaOf(pointedDirection, ?Pose)?Poses
2. member(?P, ?Poses)
3. Pose(CloseToSOF, ?P)
4. beVisible(ForHumanLeader)
F. Yazdani18. July 2014
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Artificial Intelligence
Effect-based Action Parameterization
setof ?Pose InAreaOf(pointedDirection, ?Pose) ?Poses ∧ member(?P,?Poses) ∧ Pose(CloseToSOF, ?P) ∧ beVisible(ForHumanLeader)
1. setof ?PoseInAreaOf(pointedDirection, ?Pose)?Poses
2. member(?P, ?Poses)
3. Pose(CloseToSOF, ?P)
4. beVisible(ForHumanLeader)
F. Yazdani18. July 2014
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Artificial Intelligence
Effect-based Action Parameterization
setof ?Pose InAreaOf(pointedDirection, ?Pose) ?Poses ∧ member(?P,?Poses) ∧ Pose(CloseToSOF, ?P) ∧ beVisible(ForHumanLeader)
1. setof ?PoseInAreaOf(pointedDirection, ?Pose)?Poses
2. member(?P, ?Poses)
3. Pose(CloseToSOF, ?P)
4. beVisible(ForHumanLeader)
F. Yazdani18. July 2014
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Artificial Intelligence
Effect-based Action Parameterization
setof ?Pose InAreaOf(pointedDirection, ?Pose) ?Poses ∧ member(?P,?Poses) ∧ Pose(CloseToSOF, ?P) ∧ beVisible(ForHumanLeader)
1. setof ?PoseInAreaOf(pointedDirection, ?Pose)?Poses
2. member(?P, ?Poses)
3. Pose(CloseToSOF, ?P)
4. beVisible(ForHumanLeader)
F. Yazdani18. July 2014
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Artificial Intelligence
Effect-based Action Parameterization
setof ?Pose InAreaOf(pointedDirection, ?Pose) ?Poses ∧ member(?P,?Poses) ∧ Pose(CloseToSOF, ?P) ∧ beVisible(ForHumanLeader)
1. setof ?PoseInAreaOf(pointedDirection, ?Pose)?Poses
2. member(?P, ?Poses)
3. Pose(CloseToSOF, ?P)
4. beVisible(ForHumanLeader)
F. Yazdani18. July 2014
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Artificial Intelligence
Cognition-enabled Control System
F. Yazdani18. July 2014
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Artificial Intelligence
Knowledge Framework System
• access to information, e.g. agents capabilities• task of scheduling and organising• assign information to performing robots
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Artificial Intelligence
Cognition-enabled Control System
F. Yazdani18. July 2014
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Artificial Intelligence
Requests for Robot Capabilities
• request all capabilities of the rover
?− c a p a v a i l a b l e o n r o b o t ( Cap , Rover ) .Cap = A r m M o t i o n C a p a b i l i t y ;Cap = B a s e M o t i o n C a p a b i l i t y ;Cap = R e c h a r g e Q u a d r o p t e r C a p a b i l i t y ;
• request a robot with a specific capability
?− c a p a v a i l a b l e o n r o b o t (r e c h a r g e Q u a d r o p t e r C a p a b i l i t y , Rob ) .
Rob = Rover ;
• request missing capabilities for actions
?− m i s s i n g c o m p f o r a c t i o n ( r e c h a r g e−q u a d r o p t e r ,RoverWithoutBox , Comp ) .
Comp = Q u a d r o p t e r D o c k i n g S t a t i o n ;
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Artificial Intelligence
Conclusions & Future Work
• cognition-enabled control framework for aheterogeneous team
• planinterpretation for different robots
– reasoning about capabilities of robotsystems
• interpretation of multimodal instructions
– reasoning with a generative modelapproach
• scheduling and organising robots withACMS
• Instead of using basic functions,using complex functions!
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Artificial Intelligence
Thank you for your attention!
F. Yazdani18. July 2014
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