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Intelligent Robotics. Jeremy Wyatt Thanks to: Nick Hawes, Aaron Sloman, Michael Zillich, Somboon Hongeng, Marek Kopicki. The Whole Iguana. AI commonly studies aspects of intelligence separately: narrow domain high performance

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Intelligent robotics

Intelligent Robotics

Jeremy Wyatt

Thanks to: Nick Hawes, Aaron Sloman, Michael Zillich, Somboon Hongeng, Marek Kopicki


The whole iguana
The Whole Iguana

  • AI commonly studies aspects of intelligence separately:

    narrow domain high performance

  • In 1976, philosopher Dan Dennett suggested putting it all together, but with a low level of performance

  • In fact people had been trying to build

    integrated systems for some twenty years by

    then


Shakey the robot
Shakey the robot

  • 1970 - Shakey the robot reasons about its blocksBuilt at Stanford Research Institute, Shakey was remote controlled by a large computer. It hosted a clever reasoning program fed very selective spatial data, derived from weak edge-based processing of camera and laser range measurements. On a very good day it could formulate and execute, over a period of hours, plans involving moving from place to place and pushing blocks to achieve a goal.

    (Hans Moravec)


Shakey key ingredients

30

20

10

0

r2

f4 f3

o1

d2

r3

f2

f1

d1

shakey

r1

0 10 20

Shakey: key ingredients

  • World model used logical representations

    type(r1,room)

    in(shakey,r1)

    in(o1,r2)

    type(d1 door)

    type(o1 object)

    type(f3 face)

    type(shakey)

    at(o1 15.1 21.0)

    joinsfaces(d2 f3 f4)

    joinsrooms(d2 r3 r2)


Planning

30

20

10

0

r2

f4 f3

o1

d2

r3

f2

f1

d1

shakey

r1

0 10 20

Planning

  • Shakey used a form of planning called goal regression

  • Idea: find an action that directly achieves your goal, and then actions to achieve the first action’s preconditions, etc…

  • e.g. Blocked(d1,X)

  • Let’s see Shakey solve a problem

block_door(D,Y)

preconditions: in(shakey,X) & in(Y,X)

& clear(D) & door(D)

& object(Y)

delete list: clear(D)

add list: blocked(D,Y)


Lessons from nature
Lessons from nature

  • Gannets – wings half open to

    control dive

  • Fold wings to avoid damage

  • Not at a constant distance, but at

    a constant time

  • Birds have detectors that

    calculate time to impact


Lessons from nature1
Lessons from nature

  • All naturally occuring intelligence is embodied

  • So robots are in some ways similar systems

  • Robots, like animals exploit their environments to solve specific tasks

    “There are no general purpose animals … why should there be general purpose robots?”

    David MacFarland


Behaviour based robots
Behaviour Based Robots

  • Inspired by simpler creatures than

    humans

  • Throw away most representations

  • Throw away most reasoning

  • Build your robot out of task specific behaviours


Pushing the behaviour based envelope
Pushing the behaviour based envelope

  • Behaviour based systems can display quite sophisticated behaviour, particularly for interaction

  • But they don’t have understanding because they don’t have representations


The age of data
The age of data

  • In the 1990s people were finally beginning to have success with representation driven approaches

  • One key has been the use of probabilistic methods

  • These are data intensive and require very strong assumptions about the learning task

  • Stanley


Robots that understand

B1:phys-object ^ ball

<property>

C1:colour ^ orange

Robots that understand

  • Internal structure to represent the meaning of the utterance

    e.g. “The orange ball”




Cognition requires attention
Cognition requires attention

  • Object recognition is unreliable and expensive

  • We can use bottom up salience to make it more efficient


Salience can be modulated by language

Directing processing of the visual scene

Salience can be modulated by language


The whole iguana coming full circle

Manager

Sensor

Actuator

Processor

Working

Memory

The Whole Iguana: coming full circle

  • Collection of loosely coupled sub-architectures

  • Each sub-architecture contains processing elements that update structures within a working memory

  • WM are typically only locally read & write (bar privileged sub-architectures)

  • Processing controlled by local and global goals and managers

  • Knowledge management by local and global ontologies


Illustration: Cross Modal Ontology Learning Architectures

Movie goes here


Linguistically driven manipulation

Communication SA Architectures

Communication SA

Communication SA

Communication SA

Visual SA

Planning SA

Manipulation SA

Communication SA

Binding SA

Spatiotemporal SA

Coordinator SA

Communication SA

Communication SA

Communication SA

Illustration: Language Driven Manipulation Architectures

Linguistically Driven Manipulation

  • Goals are raised by language

  • Reference to objects by learned features

  • Robot plans intentional actions using logical planner

  • Intention shifting is handled via execution monitoring and continual replanning


“Put the blue thing to the right of the red thing” Architectures

Parse + Dialogue Interpretation

Execution Check

Qual Spatial Relations

Qual Spatial Relations

Qual Spatial Relations

Object locations

Object locations

Object locations

Inst 2

Inst 2

Inst 2

Inst 1

Inst 1

Inst 1

SO 2

SO 2

SO 1

SO 2

SO 1

SO 1

Vis Servo

Raise Planning Goal

Goal LF

ROI 2

ROI 2

ROI 1

ROI 1

Raise Manip Goal

Manip Goal Executed

Plan Step

ROI 2

ROI 1

Plan

MAPL Goal

Communication SA

Coordination SA

Manipulation SA

Planning SA

Spatial SA

Binding SA

Visual SA


Illustration: Language Driven Manipulation Architectures

Movie goes here


Wrap up
Wrap up Architectures

  • Robotics gets to the heart of big issues in AI

  • There are enormous engineering and scientific challenges

  • There is a tension between different approaches:

    • Representation heavy, top-down approaches to cognition

    • Representation light, bottom approaches

  • The fun is in linking these


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