Behavior-Based Roboticsbaibook.epfl.ch/slides/behaviorBasedRobotics-slides.pdf · Methods, and...
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Behavior-Based Robotics
Companion slides for the book Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies by Dario Floreano and Claudio Mattiussi, MIT Press
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A.I. Robotics
The keystone ideas behind this approach are:
• Representations, Reasoning, Planning• Model Building (for example, geometric maps)• Functional Decomposition, Hierarchical systems• Symbol manipulation
sensors actuators
motor control
task executionplanningm
odelingperception
In traditional Artificial Intelligence robot brains are serial processing units.
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Behavior-Based Robotics (Brooks, 1996)
sensors actuators
manipulate the worldbuild maps
exploreavoid hitting things
locomote
The Behavior-Based approach states that intelligence is the result of the interaction among an asynchronous set of behaviors and the environment.
The keystone ideas behind this approach are:
• Embodiment• Situatedness• Emergent complexity• No planning
S R
S R
S R
S R
S R
S R
S R
Companion slides for the book Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies by Dario Floreano and Claudio Mattiussi, MIT Press
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A Paradigm Shift
Behavior-Based paradigm affects both software and hardware design
• Thinking and reasoning Acting and behaving• Seat of intelligence: brain Seat of intelligence: organism
• Artificial Intelligence Artificial Life
• Information processing Sensory-motor coordination
• Cartesian thinking Agent-centered; action based
Shakey, 1970, Stanford
Ghenghis, 1985, MIT
Companion slides for the book Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies by Dario Floreano and Claudio Mattiussi, MIT Press
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Priorities for RoboticsFrom Brooks, 1998
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• A behavior is a reaction to a stimulus
stimulus BEHAVIOR response
TURN-RIGHT
A behavior
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• Exploration/directional behaviors (move in a general direction)heading based, wandering• Goal-oriented appetitive behaviors (move towards an attractor)discrete object attractor, area attractor• Aversive/protective behaviors (prevent collision)avoid stationary objects, elude moving objects (escape), aggression• Path following behaviors (move on a designated path)road following, hallway navigation, stripe following• Postural behaviorsbalance, stability• Social/cooperative behaviorssharing, foraging, flocking• Perceptual behaviorsvisual search, ocular reflexes• Walking behaviors (for legged robots)gait control• Manipulator-specific behaviors (for arm control)reaching, moving• Gripper hand behaviors (for object acquisition)grasping [from Arkin, 1998]
Examples of behaviors
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BEHAVIORALMODULE
S
I
R
INPUTLINES
OUTPUTLINES
reset
inhibition
suppression
Augmented Finite State Machine- local computation- mappable into hardware- no global clock, memory, bus- no central models
A Behavioral Module (Brooks, 1986)
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Subsumption architecture (Brooks, 1986)
Col
lisio
n av
oida
nce
• The architecture is built incrementally• Start by building in lowest level of competence• Validate on robot, debug, adjust, validate, adjust, …• Robot is immediately operational
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Subsumption architecture (Brooks, 1986)
navi
gatio
n• Novel layer exploits (subsumes) earlier competence• Earlier behaviors are not modified• Design, test, debug, adjust, test, adjust,…
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Subsumption architecture (Brooks, 1986)
expl
orat
ion
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AssessAgent-Environment
Dynamics
PartitionInto Situations
CreateSituationalResponses
Import Behaviorsto Robot
Run RoboticExperiments
EvaluateResults
Enhance,Expand,Correct
BehavioralResponses
Done
Methodology
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Two robots must get to the opposite end of a narrow corridor.
Conflict resolution
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Courtesy of Applied AI Systems, Inc.
Behavioral outcome
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TAO1 - Courtesy of Applied AI Systems, Inc.
Intelligent wheelchair
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16Laboratory of Intelligent Systems http://lis.epfl.ch
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Architecture
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Behavior Coordination
Competitive methods:priorityaction-selectionvote-based
Cooperative methods:fusion
obstacle avoidance
seek gas leak
battery recharge
tele-operation
Behavior coordinationIn addition to Subsumption Architecture, there are a few other ways of coordinating behaviors
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behavior 1
behavior 2
behavior 3
behavior 4Response of behavior
With highest level of subsumptionPRIORITY-BASED
PERCEPTION
Priority Based (subsumption)
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MAX[act(b1),act(b2),act(b3),act(b4)]
behavior 1
behavior 2
behavior 3
behavior 4
Response of behavior with highest activation level
ACTION SELECTION
PERCEPTION
Action Selection (Maes, 1989)
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behavior 1
behavior 2
behavior 3
behavior 4
Response with most
votes
VOTE-BASED
PERCEPTION
R1
R2
R3
MAX[votes(R1), votes(R2),votes(R3)]
Vote Based
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behavior 1
behavior 2
behavior 3
behavior 4
Fused(weighted)response
BEHAVIOR FUSION
PERCEPTION
R1
R2
R3
R4
Σ
Often implemented asa neural network
Fusion
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MIT Artificial Intelligence Laboratory
video clips
MIT historical behavior-based robots
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Comparison (Arkin, 1998)
Traditional Behavior-based
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AIBO familySony
Intelligence based on behavior technology
Speech and touch interaction
Excellent mechanics
Learning abilities (walk)
Mood change
Remote control
Behavior sticks
Picture snapshot
Robot-to-robot interaction
Applications: entertainment
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Helpmate
no magnetic tracksprogrammable pathinteractive, radio-link
CoworkeriRobot
obstacle avoidanceinternet video conferenceoffice surveillance
MinervaCMU
stored programmable mapobstacle avoidanceinformation delivery on screenvoice interaction
Applications: assistants
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Construction robotApplied AI Systems
color vision trackingobstacle avoidanceactive beamers
Agriculture mateApplied AI Systems
obstacle avoidanceactive beamers
Intelligent WheelchairApplied AI Systems
vision-based navigationbehavior-based controlinteractive navigation
Applications: transport
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NomadCMU
mobile cameraspectrometermagnetometercompassmineral sampling
PackbotiRobot
all terrain, including stairs3 mt fallradio steeringcamera
ArieliRobot
underwater, crab-like motiondouble-sidedcan be fitted with sensors
Applications: exploration
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COG, MIT
Dream, Sony
Fujitsu
Pino, Sony Kismet, MIT
Honda
Applications: R&D humanoids
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• Intelligence is in the eye of the observer
• The world is its own best model
• Simplicity is a virtue
• Planning is a way of avoiding figuring out what to do next
• Robustness in the presence of noise or failing sensors is a design goal
• Systems should be built incrementally
• No representations. No calibration. No complex computers.
• No high-bandwidth communication
Closing remarks (Brooks)
http://lis.epfl.ch/podcast
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