Introduction to Robotics

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1

Introduction to Robotics

13. Deliberative and Hybrid
Control.

2

Deliberative Systems


Purely deliberative systems are considered the
classical control
architecture
, since they were the first to be tried.


In AI, classical deliberative,
planner
-
based architectures were used for
reasoning

about actions in various non
-
physical domains, such as
chess.



As a result, the same architectures were applied to robotics as well.

In the 1960’s: Shakey



In the late 1960's, the
state
-
of
-
the
-
art in machine vision

was used to
process visual information on a robot called Shakey, the forerunner of
many AI
-
inspired robotics projects.


Shakey used a
classical planner

as the underlying structure to decide
what to do.


What is planning?


3

Traditional Deliberative Planners


Are often aligned with
hierarchical control

community
within robotics.


Hierarchical planning

systems typically
share a structured

and clearly
identifiable subdivision

of functionality
regarding to
distinct program modules

that
communicate

with each other in a
predictable and predetermined
manner
.


At a hierarchical planner’s highest level, the
most global
and least specific
plan

is formulated.


At the lowest levels,
rapid real
-
time

response

is required,
but
the planner is concerned only

with
its immediate
surroundings

and has lost the sight of the big picture.

4

Strategic

Global

Planning

Tactical

Intermediate

Planning

Short
-
Term

Local

Planning

Actuator

Control

Actions

Global

Knowledge

Local
World

Model

Intermediate

Sensor

Interpretations

Sensing

Real
-

Time

Time

Horizon

Long
-

Term

Spatial

Scope

Global

Immediate

Vicinity

Hierarchical

Planner

World Model

5

Planning as Search


Planning is
looking ahead
,
searching



The goal
is a state
.


The robot's entire state space is enumerated, and searched,
from the current state to the goal state.



Different paths are tried until one is found that reaches the
goal.


If the optimal path is desired,
then all possible paths must
be considered

in order to find the best one.


6

SPA = Planner
-
based


Planner
-
based (deliberative) architectures typically involve three
generic sequential steps or functional modules:

1) sensing (S)

2) planning (P)

3) acting (A), executing the plan


Thus, they are called SPA architectures.


SPA has serious drawbacks.



It takes a
very

(prohibitively)
long time

to search in a real
robot's state space, as that space is typically very large.



Real robots may
have collections of simple digital
sensors

(e.g., switches, IRs), a
few more complex ones

(e.g., cameras), or
analog sensors

(e.g., encoders, gauges,
etc.)


=>
"too much information"



=>
Generating a plan is slow.

Problem
1
:

Time Scale

7

SPA = Planner
-
based


It takes a
lot of

space (
memory
)
to represent

and
manipulate the
robot's state space representation
.


The representation must
contain all information needed
for planning.


=>
Generating a plan can be large
.


Space is not nearly as much of a problem as time, in
practice.


Problem
2
:

Space

Problem 3:

Information


The
planner assumes

that the representation of the state
space
is accurate and up
-
to
-
date
.


=> The representation
must be
constantly updated and
checked


The more information, the better.



=>
"too little information"


8

SPA = Planner
-
based

The resulting plan is only useful if
:

a) the environment
does not change

during

the
execution
of a plan

in a way that
affects the plan.


b) the representation
was accurate enough

to generate
a
correct plan.


c) the robot's effectors
are accurate enough

to
perfectly
execute

each
step of the plan

in order to
make

the
next step possible

Problem
4
:

Use of Plans

Deliberation in Summary


In short, deliberative (SPA, planner
-
based) approaches:


require
search and planning
, which
are slow



encourage open
-
loop plan execution, which is
limiting and dangerous


Note that
if planning were not slow

(computationally expensive)
then
execution would not need to be open
-
loop, since re
-
planning could be done
.

9

Hierarchical Planners vs. BBS

Hierarchical Planners


Rely heavily on world models,


Can readily integrate world knowledge,


Have a broad perspective and scope.

BB Control Systems


afford modular development,


Real
-
time robust performance within a changing world,


Incremental growth


are tightly coupled with arriving sensory data.

10

Inventing Hybrid Control


The basic idea is simple
: we want the best of both worlds
(if possible).


The goal is to

combine
closed
-
loop

and
open
-
loop

execution.


That means to
combine reactive and deliberative
control.



This implies
combining

the
different

time
-
scales and
representations.


This mix is called hybrid control.



Hybrid robotic architectures

believe that a union of deliberative and
behavior
-
based approaches can
potentially yield the best of both worlds
.

11

Organizing Hybrid Systems

Planning and reaction can be tied:

A
:
hierarchical integration
-

planning and reaction are involved
with
different activities, time scales

Level N

Level
2

Level
1

Level
0

More Reactive

More Deliberative

A

Deliberation

Pr潪oc瑩潮

Planner

Reactor

B

Behavioral Advice

Configurations

Parameters

B
:
Planning to guide reaction
-

configure and set parameters

for the
reactive control system.

C
:
coupled
-

concurrent activities

Planner

Reactor

C

12

Organizing Hybrid Systems


In summary, a modern hybrid system typically consists of three components:


a reactive layer


a planner


a layer that puts the two together
.



=>
Hybrid architectures are often called

three
-
layer architectures
.



It was observed that the emerging architectural design of choice
is:



multi
-
layered hybrid comprising of

*

a
top
-
down

planning system

and

*

a
lower
-
level

reactive system
.



the interface

(middle layer between the two components)
design
is
a central issue in differentiating different hybrid architectures.

13

The Magic Middle


The middle layer has a hard job:

1
)
compensate for the limitations

of both the planner and the reactive
system

2
) reconcile their
different time
-
scales.


3
) deal with their
different representations.


4
) reconcile
any contradictory

commands
between the two.


This is
the challenge

of hybrid systems

=>
achieving the right compromise between the two ends.



14

The middle layer services.


Some frequently useful planned decisions

may need to be reused
,
so to avoid planning,
an intermediate layer
may cache

and look those
up
.
These can be:


Reusing Plans



intermediate
-
level actions

(ILAs):
stored in contingency tables
.



macro operators
: plans
compiled into more general operators

for future use.

Dynamic Re
-
planning


Reaction

can influence

planning
.


Any
"important" changes

discovered by the low
-
level controller

are
passed back to the planner
in a way that

the
planner can use to re
-
plan
.


The planner
is interrupted

when
even a partial answer

is needed in real
-
time.


The
reactive controller

(and thus the robot)
is stopped

if it must wait

for
the planner to tell it
where to go
.

15

Planner
-

Driven Reaction


Planning
can also influence

reaction.


Any
"important" optimizations

the planner discovers

are passed
down to the reactive controller.


The planner’s
suggestions

are used if they are

possible and safe.


=> Who has priority, planner or reactor? It depends, as we will see...

The middle layer services.

Types of

“Reaction


Planning”

Interaction



Selection
: Planning is viewed as configuration.



Advising
:

Planning is viewed as advice giving.



Adaptation
: Planning is viewed as adaptation of controller.



Postponing
: Planning is viewed as a least commitment process.

16

Universal Plans


Suppose for a given problem,
all possible plans
are generated

for all
possible situations in advance
, and stored.


If for each situation

a robot has
a pre
-
existing optimal plan
,
it can react
optimally
, be reactive and optimal.


It has a universal plan (These are complete reactive mappings).

Viability of Universal Plans



A system with a universal plan
is reactive
; the planning
is done at
compile
-
time
,
not at run
-
time
.


Universal plans are
not viable in most domains
, because:


the
world

must be

deterministic
.


the
world

must
not change
.


the
goals

must
not change
.


the
world

is
too complex

(state space is too large).


17

Situated Automata


A formal notion of
finite state machines


whose
inputs

are connected
to sensors

and


whose

outputs
are connected
to effectors


are called situated automata
.



Situated

means
existing in

and
interacting with

a complex world, and
automata

is
the formal name for FSMs (formally: finite state automata).



Situated automata

are used to create reactive

principled control systems.



Developed by Kaelbling & Rosenschein ‘
91
. We already mentioned situated automata in BBS.

State

Register

Sensor

Input

Action

The basic structure of Rosenschein’s situated
automata design.

18

Control with Situated Automata

Situated automata can be constructed in two basic ways:

o
By hand

(i.e., the designer puts FSMs together), as in the
Subsumption Architecture).

o
By pre
-
compiling a complete plan

(similar to Universal Plans,
but reduced down to circuits of FSMs).


Rosenschein was able to
design a compiler

that
generates finite state machines whose


internal states

can be
proved
to correspond
to certain logical
propositions about the environment
,


provided that
the initial state and the correct laws of
“physics

are given to the compiler
.

19

Situated Automata



Rosenschein’s

basic design
relies on a theorem

to the effect that
any

finite state
machine
can be implemented

as a state register
together with

a feedforward
circuit
that updates the state

based on the
sensory inputs and the current state
, and
another circuit
that calculates the output

given the state register.

Flakey, the robot capable in
1984
to navigate the halls of SRI was
based on situated automata.

This requires
the use of a special programming language

that implements the
right semantics and compiles down into FSM circuitry, as Rex and Gapps
.


20

Pre
-

compiled Systems


A key advantage of pre
-
compiled systems is that domain knowledge,
i.e., information that the designer has about the environment, the robot,
and the task
,
can be embedded into the system in a principled way
.


Then,
the system is compiled into a reactive circuit
, so the knowledge
does not have to be reasoned about

(or planned with)
explicitly, in
real
-
time.

Advantage:

Domain Knowledge

Disadvantages



A key disadvantage of pre
-
compiled systems is that
it quickly becomes
prohibitively large

to enumerate the state space of a real robot, and thus
pre
-
compiling generally
does not scale up to complex systems
.



Another disadvantage is common to compiled or hard
-
wired systems:
the result is not flexible

in the presence of
changing environments, tasks
or goals.

21

Summary: Hybrid Control Relative Strength.


Deliberative planners:


Rely heavily on
world models
.


Can readily
integrate world knowledge.



Have
broader perspective and scope.



Reactive & behavior
-
based systems:


Afford
modular development
.


Provide
real
-
time robust performance

in dynamic world.



Provide for
incremental growth.



Tightly coupled

to
incoming sensory data.


22

Representative Hybrid Architectures.


Selection
: Planning is viewed as configuration.



Advising
:

Planning is viewed as advice giving.



Adaptation
: Planning is viewed as adaptation of controller.



Postponing
: Planning is viewed as a least commitment process.

Let’s discuss an example of each of these strategies:

*

AuRA

for selection,

*

Atlantis

for advising,

*

Planner
-

Reactor

for adaptation, and

*

PRS

as postponent.

23

Example: AuRA


R. Arkin (
1986
)


Planning is viewed as configuration.



Initial A* planner
integrated with schema
-
based

controller.


Provides
modularity, flexibility, and adaptability
.

Learning

User Input

Plan Recognition

User Profile

User Intentions

Spatial Learning

Spatial Goals

Opportunism

On
-
line

Adaptation

Teleautonomy

Mission

Alterations

Mission Planner

Spatial Reasoner

Plan Sequencer

RE

PRE

SEN

TA

TI

ON

Schema Controller

Motor Perceptual

Actuation

Sensing

Hierarchical

Component

Reactive

Component

24

AuRA


Planning and execution components:


a
hierarchical system



mission planner, spatial reasoner, and
plan sequencer.

*

traditional : highest level is a mission planner

establishes
high
-

level

(global) goals + constraints
.



coupled with a
reactive system



the schema controller.

*

mission planner

acts as
an interface to human operator.


o

In the original implementation it was a
rule based system


o

Now a
finite state sequencer

was implemented.

*

spatial reasoner

navigator, uses
knowledge stored in LTM

to


construct
a sequence of path legs

that the robot must execute.

*

plan sequencer (pilot)

translates each path leg into a set of motor

behaviors for execution.



The collection of behaviors (schemas), specified by the sequencer, is sent to
the reactive system for execution.



At this point,
deliberation

ends/stops
, and
reactive execution

begins.


25

A* Search


Best
-
first search using
f

as the evaluation function

and an
admissible
h

function
.


Evaluation function
f (n)=
estimated cost of the cheapest
solution through

node n.

f(n)= g(n)+ h(n)


g(n)

gives the path cost from start to node n


h(n)

is the estimated cost of the cheapest path from n to the goal.



If
h

never overestimates
the cost is called
admissible
heuristic.


Example: estimate the distance between to cities by rhe straight
-
line distance.


A*


exhibit monotonicity

(along any path from the root

f

never decreases).


optimally efficient

(algorithm that extend search paths from the root).

26

Example: Atlantis


E. Gat (
1991
)


Three layers:
controller, sequencer, deliberator.



Asynchronous
,
heterogeneous
: reactivity and deliberation


Planning as advice giving
, not as command (not a decree).


Tested on NASA rovers.

Control

Sensors

Actuators

Deliberative

Sequencing

Results

Activation

Status

Invocation

27

Atlantis


Control Layer
:


Reactive controller
charged with managing collection of primitive

activities.


Implemented in ALFA, a LISP based program language, similar to Rex, a
circuit based language
.



Sequencing Layer:



Modeled after
RAP

(
Reactive Action Packages, Firby
1989
).



RAP is a
situation
-
driven execution reactive method
, in which the current
situation
provides an index

into a
set of actions

regarding how to act in that
environment.



Conditional sequencing

occurs upon the completion of subtasks or
detection of failure.



Notion of
cognizant failure
was introduced, referring to the
robot’s ability
to recognize on its own when it has not or cannot complete its task
: it has
knowledge

about its failures.

*
Monito
r (
task specific
)

routines

are added to determine if things are
going as they should and then interrupt the system if cognizant failure
occurs
.

28

Atlantis


Deliberative Layer.


Deliberation occurs
at the sequencing layer’ request
.


Consists of traditional
LISP based AI planning algorithms
,
specific to the
task

at hand.


The
planner’s output is viewed only as advice to the sequencer layer
: it is
not necessarily followed or implemented.


Design

proceeds from bottom up:


low
-
level activities

capable of
being executed within the reactive
controller
are first constructed.


suitable

sequences

of primitive behaviors

are then constructed.


Followed by
deliberative methods that assist

in the
decision done
by the

sequencer
.

29

Example: Planner
-
Reactor


D. Lyons (
1992
)


Continuous modification of a reactive control system (sub
-
optimal).


Planning
is a form of reactor adaptation
.


Adaptation
is on
-
line rather than off
-
line deliberation.


Planning
is used to remove performance errors

when they occur.


Uses Robot Schema (RS) model.


Tested in both assembly cell and grasp planning.

Goals

Planner

World

Adaptation

Perceptions

Reactions

REACTOR

Action

Sensing

Perception

30

Planner
-
Reactor


A
suboptimal reactor

may be present at
any time.


The
planner’s goal is to improve the performance

of the
reactor
at all times

(any
-
time planning).



Any
-
time planners

provide approximate answers

in a time
critical manner:

*

at
any point a plan is available for execution
, and

*

the
quality of the available plan increases over time
.



Situations

provide the framework for structuring sets of reactions.

*

can be defined hierarchically, as
behavioral structures
for use
in the reactor

and
not specific

robotic commands
.

*

denote
the state of the robotic agent

is currently in (regarding
a task).

31

Planner
-
Reactor


Adaptation:

a) a reactor
executes under a set

of operating assumptions.

b)
if any

assumptions are violated
,
the planner modifies the reactor’s
control system

to remove the violation.


Each assumption
has a monitor associated with it

during run time to
ensure its validity.

Reactor

Performance

with

Monitoring

Reactor

Adapted by

Planner and

Assumptions

Relaxed.

Assumptions
violation
detected.

Adapt Reactor

Restore

Initial

Reactor.

Violation
assumptions

Restored.

Completed

Initial

Reactor

Construction

Adapt Reactor

Start
Execution

Normal
Performance

32


Georgeff and A. Lansky (
1987
)


PRS =
Procedural Reasoning System


Reactivity refers to postponement
of the elaboration
of plans
until it is necessary
:


a least commitment strategy.


Tested on SRI Flakey

Example: PRS

INTERPRETER

ACTUATORS

SENSORS

MONITOR

BELIEFS

DESIRES

PLANS

INTENTIONS

COMMAND

GENERATOR

OPERATOR

INTRFACE

33

PRS


Plans

are the primary
mode of expressing action
.


They are
continuously

determined in
reaction to the current situation
.


Previously formulated
plans undergoing execution

can be interrupted
and abandoned

at any time.


Representations

of the robot’s
beliefs, desires, and intentions

are all
used to formulate a plan
.


The plan represents the
robot’s desired behaviors

instead the
traditional AI planner’s output of goal states to be achieved.


The
interpreter

drives
system execution,
handling the plan switching.


A symbolic plan always drives the system.


it is
not reactive in the normal sense

of tight sensori
-
motor pair
execution


it
is reactive in the sense

that perceived changing environmental
conditions
permit the robotic agent to alter its plans

on the fly.

38

Hybrids Everywhere?


Hybrid systems are the most popular
alternative for single
-
robot control.


Behavior
-
based systems are not used by
quite as many researchers, but have
more specialized niches (e.g., multi
-
robot systems) and more practical
applications.