introduction to ai robotics chapter 7. the hybrid deliberative/reactive paradigm 2012. 11. 14

Introduction to AI Robotics Chapter 7. The Hybrid Deliberative/Reactive Paradigm

2012. 11. 14

Hyeokjae Kwon

Objectives• Describe the hybrid paradigm in terms of 1) SPA and

2) sensing organization.• Given a list of responsibilities, be able to say whether it be-

longs in the deliberative layer or in the reactive layer.• List the five basic components of a Hybrid architecture: se-

quencer agent, resource manager, cartographer, mission planner, performance monitoring and problem solving agent.

• Be able to describe the difference between managerial, state hierarchy, and model-oriented styles of Hybrid archi-tectures.

• Be able to describe the use of state to define behaviors and deliberative responsibilities in state hierarchy styles of Hy-brid architectures.

Basic Important concept• Paradigm

– Paradigm is both a way of looking at the world and an implied set of tools for solving problems.

• Sense, Plan, Act.– Commonly accepted robotic primitives.– Robotics have to go through these three, or at

least two process to complete a mission.• Local Processing and Global World Model

– Local: sensor data used in specific for each func-tion.

– Global: all sensor data is processed to single model.

Hierarchical Paradigm• What are the two main features?

– Robot operates in a top-down fashion.– All sensor data tends to be gathered to

one global world model. A single repre-sentation that planner can use to rout the action.

SENSE PLAN ACT

Reactive Paradigm• What are the two main features?

– Throw out planning all together.– The inputs to an act are the direct out-

put of sensors.

SENSE ACT

Hybrid Paradigm• Features of Hybrid Deliberative/Reactive

Paradigm– It is reactive planning, Planning to subtask is done

at one step.– Deliberative planning take a long time comparing

to the time of reactive execution.– Sensor data go directly to each behavior but also

available to the planner for construction of task-oriented global world model.

– Model-based Architecture focuses on the creation and maintenance of a global world model.

Hybrid Paradigm• The basic models of Hybrid Paradigm

– Sequencer: generates a set of behaviors for subtasks.

– Resource manger: allocates resources to behavior

– Cartographer: for creating, storing, maintaining map or spatial information.

– Mission Planner: Interact with human and cre-ate a plan to achieve a goal

– Performance Monitoring: monitor the process of the executing, It’s self-awareness.

Hybrid Paradigm• Organization : Plan, Sense-Act

Motivation of Hybrids• Cohesion (object oriented programming)

– Reactivity:• Short time horizon (Present)• No global knowledge• Work with sensors and actuators

– Deliberation:• Long time horizon (Past, Future)• Global knowledge• Work with symbols

• Multi-tasking– Deliberative functions execute in parallel with

reactive functions.

Sensing OrganizationThe Map (World Model)

– Can have its own sensors– Can “eavesdrop” on other sensors– Can act as “virtual” sensor

World Map/ Knowledge

Rep

Behavior

Behavior

Behavior

Sensor 3

Sensor 1Sensor

2

Virtual sensor

Behavior control only

Feed-back

Planning only

Eaves-drop

Connotations of Global• “Global” isn’t always truly global in

Hybrids.• Behavioral Management

– Planning which behaviors to use requires knowledge about current and future world state

• Performance monitoring– Detecting task progress and sensor confliction require

knowledge about the robot hardware and the overall goals.

Architecture Styles• Managerial (division of responsibility as

in business)– AuRA : Autonomous Robot Architecture– SFX : Sensor Fusion Effects

• State Hierarchies (strictly by time scope)– 3T : 3-Tiered

• Model-Oriented (Model serve as virtual sensors)– Saphira – TCA : Task Control Architecture

Styles of hybrid architec-tures

● Managerial styles

● State hierarchies styles

● Model-oriented styles

Managerial Architectures• Description -- top agents – high level planning

↓ subordinate agents – refine plan, gather resources ↓ lowest level agents

▲ AuRA Architectures

▲ SFX Architectures

Autonomous Robot Architecture (AuRA)

• It consists of five subsystems -- planner : responsible for mission and task plan-ning

-- cartographer : all map making, reading functions

-- motor : motor schema

-- sensor

-- homeostatic control : modify the relationship between behaviors by changing the gain as a function of robot or other constraints

AuRA Architectural LayoutCartographer

Sequencer

MissionPlanner

Behavioral manager(mgr+schemas)

PerformanceMonitoring

Emergent behavior

reac

tive

deli b

erat

ive

The table below summarizes AuRA in term of the common components and style of emergent behavior

AuRA SummarySequencer Agent   Navigator, PilotResource Manager   Motor Schema ManagerCartographer   CartographerMission Planner   Mission Planner

Performance Monitoring Agent   Pilot, Navigator, Mission Planner

Emergent Behavior

Vector summation, spreading activation of behaviors, homeostatic control

Sensor Fusion Effects (SFX)• description – It is an extension to

AuRA. The extension was to add modules to specify how sensing and handling sensor failure.

Sensor Fusion Effects (SFX)• Deliberative layers

-- Mission planner : acts as a CEO giving a directions

-- effector

-- Task

-- Sensor All of three of above determine the best alloca-tion of effect, sensing resource and perceptual schema. -- Cartographer : map making, path planning

SFX (Sensor Fusion Effects)

Behaviors(using direct

perception, fusion)

SenseSenseSenseSenseMuscleMuscleMuscleActuators

Deliberative Layer Managers

SenseSenseSenseSensor

SenseSenseSenseReceptiveField

Choice of behaviors, resourceallocation, motivation, context

Focus of attention,recalibration

SensorWhiteboard

BehavioralWhiteboard

Del

iber

ativ

e La

yer

Rea c

tive

L ay e

r Parameters to behaviors,sensor failures, task progress

actions

SuperiorColliculus-likefunctions

CerebralCortex-likefunctions

Cartographer(model/map

making)Recognitionperception

Sensor Fusion Effects (SFX)• Reactive layers

All these layers reflect to - strategic behaviors and tactical behaviors

Tactical behavior serves as filter on strategic commands to ensure to robot acts in a safe manner in as close accordance with the strate-gic intent as possible

The interaction of strategic and tactical be-haviors is still considered emergent behavior.

Tactical Behaviorssensors strategic behaviors tactical behaviors actuators

follow-path speed-controlcamera drivemotor

avoidsonarsteermotor

center-cameracamerapanmotor

inclino-meter

slope

clutter

obstacleshow much vehicle turns

direction to path safe direction

safe velocity

swivel camera

strategicvelocity

The table below summarizes SFX in term of the common components and style of emergent behavior

SFX Summary

Sequencer Agent   Task Manager

Resource Manager   Sensing and Task Manager

Cartographer   Cartographer

Mission Planner   Mission PlannerPerformance Monitoring

Agent   Performance Monitor, Habitat Monitor

 Emergent Behavior Strategic behaviors grouped into abstract

  behaviors or scripts, then filtered by

  tactical behaviors

State-hierarchy Architectures• 3 – tiered (3T)

3 – tiered (3T)• Structure

-- planner : setting goal and strategic plans -- sequencer : select a set of primitive behaviors develop a task network -- skill manager : in this layer the skills have associated events to verify explicitly that an action has had to correct effect

3T Architecture

The table below summarizes 3T in term of the common components and style of emergent behavior

3T

Sequencer Agent   Sequencer

Resource Manager   Sequencer (Agenda)

Cartographer   Planner

Mission Planner   PlannerPerformance Monitoring

Agent   Planner

Emergent Behavior   Behaviors grouped into skills,

 skills grouped into task

network

Model-oriented Architec-tures

• Two of best-known model-oriented architecture▲Saphira Architecture▲Task Control Architecture

Saphira Architecture -- PRS-Lite it is capable of taking natural language voice com-mands from humans and then operationalizing that into navigation tasks and perceptual recognition routines.

-- virtual sensor

-- navigation tasks manage the behaviors

-- LPS (Local Perceptual Space) determine the planning and execution improve the quality of the robot’s overall behavior

Saphira Architecture

The table below summarizes Saphira in term of the common components and style

of emergent behavior

Saphira

Sequencer Agent  Topological planner, Navigation

Tasks

Resource Manager   PRS-Lite

Cartographer   LPS

Mission Planner   PRS-LitePerformance Monitoring

Agent   PRS-Lite

Emergent Behavior   Behaviors fused with fuzzy logic

Task Control Architecture (TCA)

-- Task Scheduling (Mission Planner) determine the goal and order of execution

-- Path Planning (Cartographer)

-- Navigation (Sequencer) to determine what the robot should be looking for, where it is, where it has been.

-- Obstacle Avoidance To factor in not only obstacle but how to respond with a smooth trajectory for the robot’s current velocity.

Task Control Architecture (TCA)

The table below summarizes TCA in term of the common components and style of emer-gent behavior

TCASequencer Agent   Navigation Layer

Resource Manager   Navigation Layer

Cartographer   Path-Planning Layer

Mission Planner   Task Scheduling LayerPerformance

Monitoring Agent  Navigation, Path-Planning,

Task-Scheduling

Emergent Behavior   Filtering

Thank you!