Socio-Cognitive Robot Architectures
An Exploratory Overview
15-12-2010
Lorentz Centre HART Workshop
work in progress
Koen V. Hindriks
Contact: k.v.hindriks@tudelft.nl
9-4-2015
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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Towards
Socio-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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Cognitive Robot Control Architectures
An Exploratory (and Necessarily Brief) Overview
Delft
University of
Technology
Challenge the future
A Plethora of Architectures
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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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Stanford Cart
Architecture Types
Pipeline Architectures
Based on a horizontal decomposition of functional components
Vision
Sensors
Model
Plan
Robot Platform
Execute
Control
Motors
Environment
• Classic architecture, also used for symbolic robot control architectures.
• Potential to exploit parallelism, but hard and (typically?) not used in practice.
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Architecture Types
Behavior-Based Architectures
Based on a vertical decomposition of behavior components Hannibal(MIT AI Lab)
Behavior 4, e.g. Build Map
Behavior 2, e.g. Avoid obstacle
filter
filter
Behavior 3, e.g. Explore
Behavior 1, e.g. Wander
Sensors
Robot Platform
Motors
Environment
• 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).
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Alfred B12
Architecture Types
3T or Layered Architectures
Based on a vertical decomposition of components
Deliberator
(High-level layer; planning, reasoning, …)
Sequencer
(Middle layer; conditional sequencing, sequencing constructs/language)
Controller
(Low-level layer; skills, feedback control loops)
Sensors
Robot Platform
Motors
Environment
• Classic examples: SSS (Connell 1991), ATLANTIS (Gat 1991), 3T (Bonasso 1991)
• High-level typically declarative techniques, low-level typically procedural techniques
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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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BIRON
The Bielefeld Robot Companion (2004)
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Care-O-bot 3 (Fraunhofer IPA, 2008)
Instruction model
Care-O-bot II/3
(FF)
(MySQL)
(JAM Agents)
(Realtime Framework; RTF)
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Armar
Armar (Univ. of Karlsruhe)
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 Architecture
Two-layered architecture developed at JPL/NASA
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
CLARA = Coupled Layered Architecture for Robotic Autonomy
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B21r + Katana arm
CoSy Architecture Schema
Need for easy methods for linking modules using different
forms of representation, without excessive run-time overhead
integration
mechanisms =
architectural schema
+
binding information
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Summarizing: Some key challenges
Delft
University of
Technology
Challenge the future
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
A well-defined architecture facilitates reuse and parallel development
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
•…
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Basic Abstract Architecture Design
Reducing the complexity?
Delft
University of
Technology
Challenge the future
Abstract Architecture (1/2)
Based on a vertical decomposition into functional layers
Cognitive Layer
P1
P2
Sensors
…
Behavioral Layer
Robot Platform
B1
B2
…
Motors
Environment
• P1, P2, … = process 1, process 2, …; B1, B2, … = behavior 1, behavior 2, …
• Cognitive functions supported in cognitive layer, e.g. reasoning, planning, memory, …
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Abstract Architecture (2/2)
Simple interface between cognitive and behavioral layer
Cognitive Layer
Stop …
Activate … … behavior
Override …
Symbolic representations
P1
P2
…
Behavioral Layer
B1
B2
…
• …
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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
GOAL agent program
GOAL agent architecture
See also: http://mmi.tudelft.nl/~koen/goal.html.
April 9, 2015
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DOD Levels of Autonomy
http://www.fas.org/irp/program/collect/uav_roadmap2005.pdf
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•
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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
Soar
Scott D. Hanford, Oranuj Janrathitikarn, and Lyle N. Long, 2009, Control of Mobile Robots Using the
Soar Cognitive Architecture
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ACT-R 6.0 Architecture
Current
Goal
ACT-R 6.0
Modify
Declarative
Memory
Retrieve
Check
Test
Pattern Matching
And
Production Selection
Check
State
Motor
Modules
Schedule
Action
Identify
Object
Move
Attention
Perceptual
Modules
Environment
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Cognitive Architectures Overview
SS-RICS (2006)
• SS-RICS = Symbolic and Subsymbolic Robotics Intelligence
Control System
• An extension of ACT-R
• U.S. Army Research Laboratory, Aberdeen (Kelley and Avery)
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Cognitive Architectures Overview
ADAPT (2004)
• ADAPT (Benjamin, Lyons, and Lonsdale 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.
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