Outline: Biological Metaphor

• Biological generalization• How AI applied this • Ramifications for HRI• How the resulting AI architecture relates to

automation and control theory

HRI 1

HRI 2

Biological Intelligence*

“Upper brain” or cortexReasoning over information about goals

“Middle brain”Converting sensor data into information

Spinal Cord and “lower brain”Skills and responses

*An amazingly sweeping generalization for the purpose of metaphor

HRI 3

Programming Modules

SENSE PLAN ACT

LEARN

PRIMITIVES

12

3

Early AI Robotics (1967-86)

HRI 4

PLANSENSE ACT

Seemed to capture cognitive notions such as “action-perception cycle”

12

3

Early Problem (1967-86)

HRI 5

PLANSENSE ACT

In practice: World model was intractable

In theory: Ignored Gibson, reflexes, …

HRI 6

Two Major “Loops”:1- Reflexes, Reactive, Direct Perception

Spinal Cord and “lower brain”Skills and responses, behaviors

*An amazingly sweeping generalization for the purpose of metaphor

HRI 7

Two Major “Loops”:2- Deliberative, Use Symbols/Representations

…

“Upper brain” or cortexReasoning over information about goals

Spinal Cord and “lower brain”Skills and responses

*An amazingly sweeping generalization for the purpose of metaphor

HRI 8

Plus Perception to Symbols(Abstraction, Models, Explicit Representation)

“Upper brain” or cortexReasoning over information about goals

“Middle brain”Converting sensor data into information

Spinal Cord and “lower brain”Skills and responses

*An amazingly sweeping generalization for the purpose of metaphor

HRI 9

AI Architecture Using Biological Metaphor

“Upper brain” or cortexReasoning over information about goals

“Middle brain”Converting sensor data into information

Spinal Cord and “lower brain”Skills and responses

Reactive (or Behavioral)Layer

DeliberativeLayer

Behavioral Layer

HRI 10

ACTSENSE

ACTSENSE

ACTSENSE

Behaviors are independent, run in parallel, output is emergent

SENSE-ACT couplings are“behaviors”

Just Behavioral Layer…

HRI 11

HRI Ramifications

• Overall action is EMERGENT, a product of interaction of multiple behaviors and their response to stimulus– Not amenable to proofs, traditional guarantees of

correctness/safety

• Behaviors-only implementations aren’t optimal

HRI 12

HRI 13Introduction to AI Robotics R. Murphy (MIT Press 2000) for second edition 13

PLAN, then instantiate and monitor SENSE-ACT behaviors

Reuse sensing channels but create task-specific representations

ACTSENSE

PLAN

ACTSENSE ACTSENSE

Deliberative Layer

Example

HRI 14

Don’t Know How to Do Symbol-Ground Problem/World Models

HRI 15Introduction to AI Robotics R. Murphy (MIT Press 2000) for second edition 15

“Upper brain” or cortexReasoning over information about goals

“Middle brain”Converting sensor data into information

Spinal Cord and “lower brain”Skills and responses

*An amazingly sweeping generalization for the purpose of metaphor

HRI Ramifications

• Robots are good at computer optimization, large data set types of problems– Planning and “search”– Allocation

• Robots are not good at converting what is in the real world to symbols (which are required for deliberative functions)– Recognition is hard– Gesturing and giving directions relative to objects is hard

because no perceptual common ground

HRI 16

HRI 17

What About LEARN?

SENSE PLAN ACT

LEARN

PRIMITIVES

HRI 18

What About Other People?

*An amazingly sweeping generalization for the purpose of metaphor

HRI Ramifications

• Robots don’t learn. If they do, it is extremely limited and local to a particular activity or situation

• Natural language understanding remains elusive, so unlikely to be communicating with any depth

HRI 19

HRI 20

sense actsense actsense act

monitoring generating

selecting implementing

planWorldmodel

Deliberative Layer:•Upper level is mission generation & monitoring

•But World Modeling & Monitoring is hard (SA)

•Lower level is selection of behaviors to accomplish task (instantiation) & local monitoring

How AI Relates to Factory Automation

HRI 21

sense actsense actsense act

Reactive (fly by wire, inner loop control, behaviors):•Tightly coupled with sensing, so very fast

•Many concurrent stimulus-response behaviors, strung together with simple scripting with FSA

•Action is generated by sensed or internal stimulus

•No awareness, no mission monitoring

•Models are of the vehicle, not the “larger” world

Control Theory is “Lower Level” But Doesn’t Necessary Capture it All

planWorldmodel

HRI 22

Consider Time Scales/Horizon

sense actsense actsense act

monitoring generating

selecting implementing

planWorldmodel

PRESENT, VERY FAST, PARALLEL

PRESENT+PAST, FAST

PRESENT+PAST+FUTURE, SLOW

Recap…

HRI 23

Next: Case Studies

HRI 24