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Learning in Worlds with Objects
Leslie Pack Kaelbling
MIT Artificial Intelligence Laboratory
With Tim Oates, Natalia Hernandez, Sarah Finney
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NTT-MIT Collaboration Meeting, 2001
What is an Agent?
A system that has an
ongoing interaction with an
external environment
• household robot
• factory controller
• web agent
• Mars explorer
• pizza delivery robot
Leslie Pack Kaelbling
Environment
Observation
2
Action
NTT-MIT Collaboration Meeting, 2001
Agents Must Learn
Learning is a crucial aspect of intelligent behavior
• human programmers lack required knowledge
• agents should work in a variety of environments
• agents should work in changing environments
What to learn?
• World dynamics: What happens when I take a
particular action?
• Reward: What world states are good?
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Crisis
Current state-of-the-art learning methods will not work in
domains with multiple objects:
?
These are crucial domains for robots of the future.
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Representation
Learning requires some sort of representation of states
of the world.
The choice of representation affects
• what information can be represented
• what kinds of generalizations the agent can make
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Attribute Vector
State-of-the-art representation for learning
temperature = 48.2
pressure = 57.9 mB
valve1 = open
valve2 = closed
time = 10:48AM
backlog = 78
volume = 32.2
production = 45.5
…
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Generalization over Attribute
Vectors
x
1
0.5
0
-0.5
-1
0
3
2
time
temp > 22
1
1
2
temp
3
0
pressure < 3
close
valve
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open
valve
time < 10AM
add
reagent
increase
temp
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Complex Everyday Domains
Attribute vector is impossibly big
book1-on-book2:
book2-on-book1:
true
false
pen-is-yellow:
pen-is-blue:
lamp-on:
lamp-off:
ink-bottle-level:
true
false
true
false
50%
lamp-in-bottle:
bottle-on-lamp:
paper1-color:
paper2-color:
false
false
gray
white
fabric-behind-lamp:
book2-is-clear:
book4-is-clear:
book1-is-clear:
true
false
false
true
block1-on-block2:
block3-unstable:
block2-on-table:
block1-in-front-of-lamp:
false
true
false
true
…
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Generalization over Objects
• If book1 is on book2 and I move book2, then book1
will move
• If the cup is on the table and I move the table, then
the cup will move
• If the pen is on the paper and I move the paper,
then the pen will move
• If the coat is on the chair and I move the chair,
then the coat will move
For all objects A and B:
If A is on B and I move B, then A will move
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Referring to Objects
Traditional symbolic AI has the problem of “symbol
grounding”:
How do I know what object is named by book1?
on(book1,book2)
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Deictic Expressions
“Deixis” is Greek for “pointing”
ima
koko
watashi-ga motteiru hako
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watashi-ga miteiru hako
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Automatic Generalization
If I have an object in my hand and I open my hand, then
the object that was in my hand is now on the table
This is true, no matter what object is in your hand.
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Communicating with Humans
Natural language communication
• speaks of the world in terms of objects and their
relationships
• uses deictic expressions
Our robots of the future will have to be able to
understand and generate human descriptions of the
world
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Long-Term Research Goal
A robotic system with hand and cameras that can
• learn to achieve tasks efficiently through trial and
error
• acquire natural language descriptions of the
objects and their properties through “conversation”
with humans
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Short-Term Research Plan
Explore deictic, object-based representation for learning
algorithms
• build simulated hand-eye robot system that
manipulates blocks (with real physics)
• have simulated robot learn to carry out tasks from
trial and error
Demonstrate empirically and theoretically that deictic
representation is crucial for efficient learning
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First Example Domain
Unreliable block stacking:
• robot is rewarded for making tall piles of blocks
• the taller a pile is, the more likely it is to fall over
when another block is added
• a pile can be made more stable by building piles to
its sides
Once the robot learns to do this task, keep the physics
of the domain the same, but reward a more complex
behavior.
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Learning by Doing
Having an initial task to perform focuses the robot’s
attention on aspects of the environment
• Use extension of Utree learning algorithm to select
important aspects of the environment
• Generate new deictic expressions dynamically:
the-block-on-top-of(the-block-I-am-looking-at)
• Extend reinforcement learning methods to apply to
object-based representations
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Extracting General Rules
There are too many facts that are true in any interesting
environment.
Solving tasks focuses attention on
• particular objects (named with deictic
expressions)
• particular properties of those objects
These objects and properties are likely of general
importance: use them as input to association-rule
learning algorithm to learn facts like:
The thing that is on the thing that I am holding will
probably fall off if I move
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Enabling Planning
Given general rules, the
agent can “think” about
the consequences of its
actions and decide what
to do, rather than learn
through trial and error.
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In Future
An ambitious research project
• vision algorithms for learning segmentation and
object recognition
• learning good properties and relations for
characterizing the domain (“concept learning”)
• connect with natural language learning for word
meanings
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Don’t miss
any dirt!
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