intelligent robotics jeremy wyatt thanks to: nick hawes, aaron sloman, michael zillich, somboon...
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![Page 1: Intelligent Robotics Jeremy Wyatt Thanks to: Nick Hawes, Aaron Sloman, Michael Zillich, Somboon Hongeng, Marek Kopicki](https://reader035.vdocument.in/reader035/viewer/2022062804/56649d405503460f94a19cbf/html5/thumbnails/1.jpg)
Intelligent Robotics
Jeremy Wyatt
Thanks to: Nick Hawes, Aaron Sloman, Michael Zillich, Somboon Hongeng, Marek
Kopicki
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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
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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)
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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)
…
shakey
30
20
10
0
0 10 20
r3
f4 f3
d2
d1
f2
f1
r1
r2
o1
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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)
shakey
30
20
10
0
0 10 20
r3
f4 f3
d2
d1
f2
f1
r1
r2
o1
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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
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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
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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
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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
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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
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Robots that understand
Internal structure to represent the meaning of the utterance
e.g. “The orange ball”
B1:phys-object ^ ball
<property> C1:colour ^ orange
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Learning object appearances
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Learning names and appearances of objects
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Cognition requires attention
Object recognition is unreliable and expensive
We can use bottom up salience to make it more efficient
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Salience can be modulated by language
Directing processing of the visual scene
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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
SensorActuator
Processor
Working
Memory
Manager
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Movie goes here
Illustration: Cross Modal Ontology Learning Architectures
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Linguistically Driven Manipulation
Illustration: Language Driven Manipulation Architectures
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
Communication SA
Communication SA
Communication SA
Binding SA
Communication SA
Visual SA
Communication SA
Spatiotemporal SA
Communication SA
Coordinator SACommunicatio
n SA
Planning SA
Communication SA
Manipulation SA
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ROI 1
SO 1
ROI 2
SO 2
Inst 1
Qual Spatial Relations
Inst 2
Object locations
Communication SA
Binding SA
Visual SA
“Put the blue thing to the right of the red thing”
Parse + Dialogue
Interpretation
Coordination SA
Spatial SA
Raise Planning Goal
Goal LF
Planning SA
Object locations
Qual Spatial Relations
Object locations
Qual Spatial Relations
ROI 1
SO 1
Inst 1
ROI 2
SO 2
Inst 2
Manipulation SA
MAPL Goal Plan Plan Step
Vis Servo
Manip Goal Executed
Execution Check
ROI 1
SO 1
ROI 2
SO 2
Raise Manip Goal
Inst 1 Inst 2
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Movie goes here
Illustration: Language Driven Manipulation Architectures
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Wrap up 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