122-04-23

Socio-Cognitive Robot Architectures

Koen V. Hindriks

15-12-2010

An Exploratory Overview

Lorentz Centre HART Workshop

work in progress

Contact: k.v.hindriks@tudelft.nl 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.

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 Mapfilter

Hannibal(MIT AI Lab)

filter

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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 effortsIssues:“not invented here”“fear of unknown”“learning curve”…Observation:3T:•dominant layer?•access to info?•obscures hierarchy within layersTwo 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

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

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, …?

GOAL Agent Programming Language

April 22, 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

CheckRetrieveModify

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.