1 29-8-2015 socio-cognitive robot architectures koen v. hindriks 15-12-2010 an exploratory overview...
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119-04-23
Socio-Cognitive Robot Architectures
Koen V. Hindriks
15-12-2010
An Exploratory Overview
Lorentz Centre HART Workshop
work in progress
Contact: [email protected] Webpage:
http://mmi.tudelft.nl/SocioCognitiveRobotics
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Goal of this presentation
• Collect your feedback about some preliminary
ideas about designing / developing a socio-
cognitive robot control architecture
• I’d also like to collect some lessons learned based
on your robot development experience; e.g. which
pitfalls should be avoided.
• Please jump in! I’d appreciate teamwork ;-)
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Overview
• Exploratory overview of cognitive robot control architectures
• Basic Abstract Architecture Design
• Summarizing: Current understanding of some key challenges
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TowardsSocio-Cognitive Robot Architectures
• Challenge for cognitive architectures: real time autonomous processing needed to interact with dynamic world we live in.
• Need for socio-cognitive architectures pushed by humanoid robots that interact with humans in a multi-modal fashion.
• Towards an architecture for social interaction and teamwork• Klein, G., Woods, D. D., Bradshaw, J. M., Hoffman, R. R., & Feltovich,
P. (2004). Ten challenges for making automation a "team player" in joint human-agent activity. IEEE Intelligent Systems 19(6): 91-95.
• Here we look at various current state-of-the-art approaches, and take cognitive robot architectures as a starting point.
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Challenge the future
DelftUniversity ofTechnology
Cognitive Robot Control ArchitecturesAn Exploratory (and Necessarily Brief) Overview
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A Plethora of Architectures• Subsumption architecture (Brooks 1985)• BDL (Rochwerger et al. 1994)• RAP (Firby 1994)• TCA (Simons et al. 1997).• SSS (Connell 1991)• ATLANTIS (Gat 1991)• 3T (Bonasso 1991)• Saphira (Konolige 1996)• CLARAty (Volpe et al 2001)• CoSy schemas (Hawes et al 2007)• Soar• ACT-R (SS-RICS, …)• ADAPT• …
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Architecture TypesPipeline ArchitecturesBased on a horizontal decomposition of functional components
• Classic architecture, also used for symbolic robot control architectures.• Potential to exploit parallelism, but hard and (typically?) not used in
practice.
Stanford Cart
Environment
Robot PlatformSensors Motors
Vision Model Plan Execute Control
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Architecture TypesBehavior-Based ArchitecturesBased on a vertical decomposition of behavior components
Environment
Robot PlatformSensors Motors
Behavior 1, e.g. Wander
• Components are in competition, run in parallel and outputs are filtered by some technique.
• Reactive architectures typically do not support cognitive functions and seem to have a “capability ceiling” (Gat 1998).
Behavior 2, e.g. Avoid obstacle
Behavior 3, e.g. Explore
Behavior 4, e.g. Build Map
filte
rHannibal(MIT AI Lab)
filte
r
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Architecture Types3T or Layered ArchitecturesBased on a vertical decomposition of components
Environment
Robot PlatformSensors Motors
Controller(Low-level layer; skills, feedback control loops)
• Classic examples: SSS (Connell 1991), ATLANTIS (Gat 1991), 3T (Bonasso 1991)• High-level typically declarative techniques, low-level typically procedural
techniques
Sequencer(Middle layer; conditional sequencing, sequencing
constructs/language)
Deliberator(High-level layer; planning, reasoning, …)
Alfred B12
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Rationalizing 3T Architectures
• Erann Gat (1998) rationalized three-layer architectures by arguing there is a correspondence between layers and the role of internal state.
• Deliberator: state reflecting predictions about the future
• Sequencer: state reflecting memories about the past
• Controller: no state (stateless sensor-based algorithms)
• Responsiveness, time scale also varies over components.
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BIRONThe Bielefeld Robot Companion (2004)
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Care-O-bot II/3Care-O-bot 3 (Fraunhofer IPA, 2008)
(JAM Agents)
(FF)
(MySQL)
(Realtime Framework; RTF)
Instruction model
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Armar (Univ. of Karlsruhe)
Armar
Low-level can also access GKB
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Saphira Architecture
“No overt planning” No third (high-level) layer
LPS = Local Perceptual Space
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CLARAty ArchitectureTwo-layered architecture developed at JPL/NASA
CLARA = Coupled Layered Architecture for Robotic Autonomy
Observations:No standard no leverage of robotics’ community efforts
Issues:“not invented here”“fear of unknown”“learning curve”…
Observation:3T:•dominant layer?•access to info?•obscures hierarchy within layers
Two layers blend declarative and procedural techniques
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CoSy Architecture SchemaB21r + Katana arm
integrationmechanisms =
architectural schema+
binding information
Need for easy methods for linking modules using different forms of representation, without excessive run-time overhead
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Challenge the future
DelftUniversity ofTechnology
Summarizing: Some key challenges
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Key Problem: Integration Challenge
Observation:•Over time more and more components have been integrated into cognitive robot architectures.
Q:•How many layers?
•A Socio-Cognitive Architecture only adds to this challenge. Any ideas / approaches for effective design approaches for integrating e.g. new components for social interaction and coordination both with humans and other robots?
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Key Problem: Access to Data/Information/KB
Observation:•After classical 3T architectures, all cognitive robot architectures have a common database shared by all layers
Q:•Which data needs to be shared? Mainly localization information?
•It seems that all three-layered architectures require sharing of data by all layers. Do 2-layered architectures require this?
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Well-defined Robot Architecture
Q:
• Do general software architectural principles apply?
• What is a well-defined robot architecture? Any criteria?
Example principles:
• partition architecture into layers with well-defined interfaces
• partition code into functional blocks with well-defined inputs
and outputs
• …
A well-defined architecture facilitates reuse and parallel development
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Challenge the future
DelftUniversity ofTechnology
Basic Abstract Architecture DesignReducing the complexity?
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Abstract Architecture (1/2)
Based on a vertical decomposition into functional layers
Environment
Robot PlatformSensors Motors
Behavioral Layer
• P1, P2, … = process 1, process 2, …; B1, B2, … = behavior 1, behavior 2, …
• Cognitive functions supported in cognitive layer, e.g. reasoning, planning, memory, …
Cognitive Layer
P1 P2 …B1
B2 …
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Abstract Architecture (2/2)
Simple interface between cognitive and behavioral layer
Behavioral Layer
• …
Cognitive Layer
P1 P2 …B1
B2 …
Stop …Activate … … behaviorOverride …
Symbolic representations
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Emotion expression using gestures
Which emotion is expressed?
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The End
• I reached the end ;-)
• Any additional
questions
comments
suggestions?
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TODO
• TeradaEtAl2008, A Cognitive Robot Architecture based on Tactile and Visual Information
• Architectures don’t discuss plan repair, …?
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GOAL Agent Programming Language
April 19, 2023 31
GOAL agent program
GOAL agent architectureSee also: http://mmi.tudelft.nl/~koen/goal.html.
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DOD Levels of Autonomy http://www.fas.org/irp/program/collect/uav_roadmap2005.pdf
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• Tooth: http://www.kipr.org/robots/tooth.html • Rocky III: http://www.kipr.org/robots/rocky.html • Herbert:
http://www.ai.mit.edu/projects/mobilerobots/veterans.html • Robbie:
http://www.magneticpie.com/LEGO/roverHistory/roverSize.html
• B12 (Alfred): http://srufaculty.sru.edu/sam.thangiah/B12Robot.htm
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Cognitive Architectures Overview
Scott D. Hanford, Oranuj Janrathitikarn, and Lyle N. Long, 2009, Control of Mobile Robots Using the Soar Cognitive Architecture
Soar
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ACT-R 6.0 Architecture
MotorModules
Current Goal
PerceptualModules
DeclarativeMemory
Pattern MatchingAnd
Production Selection
Check
RetrieveModify
Test
Check State Schedule
Action
IdentifyObject
MoveAttention
ACT-R 6.0
Environment
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Cognitive Architectures Overview
• SS-RICS = Symbolic and Subsymbolic Robotics Intelligence Control System
• An extension of ACT-R• U.S. Army Research Laboratory, Aberdeen (Kelley and
Avery)
SS-RICS (2006)
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Cognitive Architectures Overview
• ADAPT (Benjamin, Lyons, and Lonsdale 2004)
ADAPT (2004)
Benjamin, P., Lyons, D., and Lonsdale, D., “Designing a Robot Cognitive Architecture with Concurrency and Active Perception,” Proceedings of the AAAI Fall Symposium on the Intersection of Cognitive Science and Robotics, October, 2004.