The Hierarchical Paradigm

Introduction to AI Robotics (MIT Press) Chapter 2: The Hierarchical Paradigm 1

2 The Hierarchical Paradigm• Describe the Hierarchical Paradigm in terms of the 3 robot

primitives and its organization of sensing

• Name and evaluate one representative Hierarchical architecture in terms of: support for modularity, niche targetability, ease of portability to other domains, robustness

• Solve a simple navigation problem using STRIPS (hint: work through Sec. 2.2.2)

• Understand precondition, closed world assumption, open world, frame problem

• List two advantages and disadvantages of the Hierarchical Paradigm

Organization-SPA-globalStrips-ShakeyRep. Arch.-evaluation-NHC-RCASummary


2 Hierarchical Paradigm Organization

PLANSENSE ACT


World model(global):1. A priori rep2. Sensed info3. Cognitive


2 Shakey

• First AI mobile robot

• Built by SRI (Stanford Research Institute) for DARPA 1967-9

• Used Strips as main algorithm for controlling what to do



2 Strips: Means-ends analysis


INITIAL STATE: Tampa, Florida (0,0)

GOAL STATE: Stanford, California (1000,200)

Difference: 1020 miles

“Go to Stanford AI Lab”

If can’t accomplish the task in one step, pick anAction that will reduce the difference betweenThe current state and the goal state


2 Difference Table

d<=200 miles FLY

100<d<200 TRAIN

d<=100 DRIVE

Distance(difference)

mode of transportation(OPERATOR)

d<1 WALK


mode=difference_table(INITIAL STATE, GOAL STATE, difference)

1. Look up what to do: FLY2. Not at SAIL, so repeat3. Look up what to do: DRIVE


2 Preconditions

d<=200 miles FLY

100<d<200 TRAIN

d<=100 DRIVE (rental)

DRIVE (personal car)

difference OPERATOR

d<1 WALK


How do I know if I’m at the airport or at home?Now must keep up with the state of the world

at airport

at home

PRECONDITIONS


2 Maintaining State of the World:Add and Delete Lists

d<=200 miles

FLY

100<d<200

TRAIN


at airport

DRIVE (personal)

at home

distance OPERATOR PRE-CONDITIONS

d<1 WALK


at city Yat airport

at city Yat train station

ADD-LIST

at city X

at city X

DELETE-LIST


2 Class Exercise

• Write down the world model, the operator applied, the change in world state, etc. to go from Tampa to Stanford


d<=200 miles

FLY

100<d<200

TRAIN


at airport

DRIVE (personal)

at home

distance OPERATOR PRE-CONDITIONS

d<1 WALK

at city Yat airport

at city Yat train station

ADD-LIST

at city X

at city X

DELETE-LIST


2 Strips Summary• Designer must set up

– World model representation– Difference table with operators, preconditions, add & delete

lists– Difference evaluator

• Strips assumes closed world– Closed world: world model contains everything needed for

robot (implication is that it doesn’t change)– Open world: world is dynamic and world model may not be

complete

• Strips suffers from frame problem – specifying what is and is not changed by performing an action– Frame problem: representation grows too large to reasonably

operate over



2 Example p,. 46-47


2 Architecture• provides a principled way of organizing a control system.

However, in addition to providing structure, it imposes constraints on the way the control problem can be solved [Mataric]

• describes a set of architectural components and how they interact [Dean & Wellman]

• Types of architectures [Levis, George Mason University]– operational architecture: describes what the systems does, not how

it does it

– systems architecture: describes how a system works in terms on major subsystems

– technical architecture: implementation details



2 Evaluating an Architecture

• support for modularity: does it show good software engineering principles?

•

• niche targetability: how well does it work for the intended application?

• ease of portability to other domains: how well would it work for other applications or other robots?

• robustness: where is the system vulnerable, and how does it try to reduce that vulnerability?



2 Hierarchical Paradigm…

• Top-down: – Plan, plan, plan

• Control-theoretic: – must measure error in order to control device

• Planning means:– dependence on world models


2 Representative Hierarchical Architectures

• Nested Hierarchical Controller (NHC)

• NIST Realtime Control System (RCS)

– Original by Albus

– Teleoperation version for JPL called NASREM


2 Nested Hierarchical Controller(Meystel)


Locates robot, goal on map

Plans path segments

Plans actions for each path

segment


2 NHC Planner



2 NHC (cont)

• After action is taken, poll sensors again, update world model

• Pilot informs navigator if waypoint is reached, if robot drifts off course or obstacle is encountered

• Navigator plans new path segments based on updated world model

• Comparison with Strips– Advantage: interleaves planning and acting so that it can

replan if the world is different than expected

– Disadvantage: appropriate only for navigation


2 RCS (Albus)

• the hierarchy – p. 58, Fig 2.7

• how the hierarchy works for navigation

• how it is implemented– nodes and modules

– planning time periods


2 Examples of RCS Apps



2 RCS-4 Levels

7 Battalion 2-24h

6 Platoon 5m-2h

5 Section 1-10m

4 Individual Vehicle 5-50s

3 Subsystem Level 200-500ms

2 Primitive Level 50-500ms

1 Servo Level 5-50ms


2 Each Level has a RCS Node

Sensory Processing, World Modeling,Behavior Generation, Value Judgment

Sensory Processing, World Modeling,Behavior Generation, Value Judgment

Engineering of Mind, Albus & Mystel, 2001


2 Implementation View:Nodes are Recursive


2 Demo III XUV

http://museum.nist.gov/exhibits/timeline/item.cfm?itemId=38

Experimental Unmanned Vehicle in action at Ft. Indiantown Gap. Photo courtesy of the Army Research Labs. Nov. 2001


2 Demo III Control HierarchyPLANNER

vehicle1 vehicle2

VEHICLE PLANNERcommunications plan AM plan RSTA plan

AM PLANNERDriver Plan Gaze plan

COMMS PLANNERmessage list

RSTA PLANNERgaze plan

DRIVER PLANNERVelocity Plan

GAZE PLANNERStereo Gaze Plan LADAR Gaze Plan

VELOCITY PLANNERF Wheels R Wheels F Steer R Steer

F Wheel R Wheel F Steer F Steer

Servo 50ms

Primitive 500ms

Subsystem 5s

Vehicle 1m

Section 10m


2 RCS XUV Example

Vehicle Level:AM Plan(A1…A10)

Primitive Level:Driver Plan(D1…D10)


2 t=0.5

Primitive Level:Driver Planextends to A2


2 t=1 s

Obstacle Detected

Primitive Level:Driver Plannew waypoints

Vehicle Level:detects too large avariation


2 t=1 s Vehicle Level Planner Opt 1

Vehicle Level:new AM Plan

Primitive Level:new Driver Plan


2 t=3

More obstacle is seen…fail upwards again

Vehicle Level:new AM Plan



2 t=4.5s

Vehicle Level:new AM Planskip A1, go to A2



2 t=6 s

Vehicle Level:new AM Planskip old A2



2 Exercise: Adapt to Rescue Robots?PLANNER

vehicle1 vehicle2

VEHICLE PLANNERcommunications plan AM plan RSTA plan

AM PLANNERDriver Plan Gaze plan

COMMS PLANNERmessage list

RSTA PLANNERgaze plan

DRIVER PLANNERVelocity Plan

GAZE PLANNERStereo Gaze Plan LADAR Gaze Plan

VELOCITY PLANNERF Wheels R Wheels F Steer R Steer

F Wheel R Wheel F Steer F Steer

Servo 50ms

Primitive 500ms

Subsystem 5s

Vehicle 1m

Section 10m


2 Nodes are made from Modules

uff applies rule(transition rules)

sensors actions

u=uff+G(xd-x*)u is control action

x* is predicted world statexd is desired world state

uff is the feedforward control plan

transform into x*G is feedbackxd is from “above”




sensors actions

if BALL, move toward centroidif NOT BALL, turn clockwise

(feedback determines how fast)





sensors actions

if BALL, move toward centroidif NOT BALL, turn clockwise

(feedback determines how fast)


when to stop?how far is far enough?what about noise/fuzzy ball?…sensor noise, actuator error,rigid models


2 Advantages of Hierarchies

Albus and Mystel 01:

• Natural way to organize• Not intrinsically rigid• Not intrinsically inefficient

– not the same as centralized planning

– priorities and goals are clear, therefore efficient


2 Summary RCS

• hierarchy with node structure at each level– have operator interface (in theory)

• nodes consist of– Sensory Processing– World Model– Behavior Generation– Value Judgment

• top-down, plan for a particular horizon– control theoretic


2 Evaluating the Two Architectures• support for modularity:

– decomposition by functionality

• niche targetability: – good, both have been used for apps like vehicle guidance,

mining equipment

• ease of portability to other domains: – unclear, not sure if code could be reused—lots of rewriting on

previous apps

• robustness:– RCA simulates plans in advance, but not sure what it would do

with sensor or mechanical failures, etc.



2 Hierarchical Review• Describe the hierarchical paradigm in terms of the

three robot primitives• Describe sensing in the hierarchical paradigm

• What is STRIPS?

• What is the closed world assumption?• What are preconditions?• What is the frame problem?

• What are two representative architectures?• What is the NHC decomposition?


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The Hierarchical Paradigm