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Adaptive RoboticsAdaptive RoboticsCOM2110COM2110

Autumn Semester 2008 Autumn Semester 2008 Lecturer: Amanda SharkeyLecturer: Amanda Sharkey

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“Robots in the news”Macho robot helps explain lizards' odd behaviour 22:00 24 November 2008 by David Robson New ScientistWhy does male anolis lizard perform a series of push-ups before attempting to intimidate rivals with colourful displays?Terry Ord at Harvard University built a robotic lizard that can inflate a colourful dewlap under its chin, bob its head, and perform push-ups

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Cameras in Puerto Rico forests recorded when lizards turned their headsRobot lizard’s push-ups attracted attention – without them lizards often missed much of coloured dewlap display.

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Lect 1: what is a robot? Early robots, Shakey and GOFAI, Behaviour-based roboticsMechanisms and robot control (and biological inspiration)Lect 2: Grey Walter, Brooks and Subsumption Architecture.Lect 3: Adaptation and learningLect 4: Artificial Neural Nets and Learning (biologically inspired, could be used to implement robot control in behaviour-based robotics approach, also move away from GOFAI)Lect 5: Evolutionary Robotics (also biologically inspired – another way of developing robot control)Lect 6: Swarm Robotics (reasons for, biological inspiration, local control and communication, self-organisation and emergence).Lect 7: Biorobotics and Biological modelling.Lect 8: Applications

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“new wave” roboticsAkaBehaviour-based roboticsNouvelle AIEmbodied Cognition

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In opposition to:Sense-Model-Plan-Action approachCentralised cognitionMind as central logic engineMemory as retrieval from stored symbolic databaseProblem solving as logical inferenceEnvironment – problem domainBody – input deviceFunctionalism

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FunctionalismThinking, and other intelligent functions – could be carried out on different hardware. - interested in the software.Swiss cheese?Physical Symbol System Hypothesis(Newell and Simon, 1975)Emphasis on manipulation and processing of symbolic representations of the worldLittle interest in how those representations are related to the objects in the world.

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Review of defining characteristics of “new wave” robotics and “nouvelle AI”

Simplicity – “Keep it simple”Minimal representationBiological inspirationEmbodimentSituatednessEmergenceAutonomyInteraction with the environment

Brain, body and world

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Two guiding principles (from Maes, 1994)

Looking at complete systems often changes the problems in a favourable wayInteraction dynamics can lead to emergent complexity

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Complete systemsBuilding complete systems can simplify problem

E.g. with sensors, easier to disambiguate natural language utterances because they are related to the objects the agent seesE.g. systems with sensors and actuators can perform tests in the environment and needs less modelling and inference

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Complete systemsSince intelligent system is situated in environment, this can be exploited

E.g. using the environment as external memory, reminding which tasks remain to be done. Also habitat constraints, e.g. usual size of doors in office, can be exploited

Time: incremental solution can be arrived at

e.g NLP and asking further questions

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Complete systemsSociety: can look at other agents and other solutions.

E.g mobile robot closely following a person walking by, to avoid bumping into things.

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Emergent complexityIdea from ethology that animal’s behaviour can only be understood in the context of the environment in which it occurs.Simon(1969) the complexity of an ant’s behaviour reflects the complexity of its environment

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Emergent functionalityE.g. Mataric’s (1991) wall following robot. One module steers robot towards wall when distance above threshold, and one module steers away when distance below threshold – result = wall followingSocial insects following simple local rules to produce emergent complexity

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Today - applicationsNavlab – autonomous vehicles

Alternative to subsumption architecture

DARPA grand challengeSwarm robotics – pherobotsRobot sheep dog Leurre project: influencing the behaviour of cockroaches

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Application areasPhysical robots

Household e.g. vacuuming, lawn mowing Autonomous vehicles e.g. Navlab AgriculturalHostile terrains e.g. underwater, space, military, bridge inspections, disaster (9/11)

Urban search and Rescue (Robin Murphy)

Entertainment e.g. toys, personal robots, exhibitions, gamesCompanions – for the young, for the elderly MilitaryPolicing and surveillance

Some ethical issues in involved in the above

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NavlabCMU (Carnegie Mellon University) groupRobot cars, trucks, buses for autonomous navigation11 different Navlabs (Navlab11 on its way)Langer, Rosenblatt and Herbert (1994) A Behaviour-based System for Off-Road Navigation. IEEE Trans Robotics and Automation, 10, 6, pp 776-782

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Navlab 11

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Autonomous cross-country navigationRugged terrainProcessing of 1000s of imagesNeed to avoid failureSimple algorithms for obstacle detection and local map building in behaviour-based architectureUnderlying principle: keep things simple

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Use of independent modulesPerception module (list of untraversable regions)Local map module (maintains map of terrain round vehicle)Planning module (generates steering arcs, keeping clear of untraversable regions)

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Perception Takes single image as input and produces list of untraversable regionsTerrain classification algorithm: based on grid systemEach cell in grid corresponds to 20cm x 20cmEach cell classified as traversable or not

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Terrain classificationStrengths: simple system, each image processed individually without terrain matching and mergingLimitations – some misclassification – dense vegetation can appear as an obstacleProblems with regions with poor reflectance e.g. waterDependence on good sensors

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Local Map ManagementPurpose – to maintain list of untraversable cells in region round vechicleModule called GaneshaUses 2D grid-based representation of local mapCore of system is single loop

Read current position of vehicle, update coordinates of cells. Discard cells outside bounds of active regionsGet obstacle cells and place in local mapUpdate internal cell attributesSend list of obstacle cells to planning system

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PlanningUse map to generate commands to steer round obstaclesUsed DAMN (Distributed Architecture for Mobile Navigation) behaviour-based architecture

Like subsumption architectureUses specialised task-achieving modules that operate independently and are responsible for only part of vehicle controlSome internal representation of worldActivation selection – relies on command fusion

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DAMN cont.Each behaviour votes for or against set of vehicle actions

Votes between –1 and +1 for each of 15 steering commandsWeighted sum of votes computed. Steering arc with maximum vote is foundSpeed also decided by votingObstacle avoidance – each behaviour has list of current obstacles

Votes for trajectories free of obstaclesVotes against paths with obstaclesOther behaviours: goals seeking, drive straight, maintain turn.

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2 behavioursObstacle avoidance and goal seekingArbiter combines votes and issues new driving command every 100 msWeights 0.8 for obstacle avoidance and 0.2 for goal seeking

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LimitationsCan’t deal with some situations e.g dead ends such as closed corridor with depth greater than field of view of sensorLimited range and speed of sensorNon-real-time naturePoor performance of perception on certain types of environment.BUT 1km traverse shows robustness

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DARPA Grand Challenge Robot Vehicle race 2005

Unmanned vehicles on 132 mile course in Mojave desert- of 23 entrants, five completed.(previous year none got further than 11 km)Winner: Stanley, Stanford University6 hours, 53 minutes(2,000,000 dollars)2nd place: Sandstorm, CMUSee www.grandchallenge.org

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Route given 2 hours before competition, in form of GPS coordinatesTeams could program routes into vehiclesSebastian Thrun – Stanley had vision-based speed switch: drove faster when it detected straight road ahead without obstacles

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Stanley

Used: GPSLaser Range Finder to map the road 30 meters aheadVideo camera to scan 80 meters aheadOdometry

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DARPA Grand Challenge 2007

“Urban challenge”“Autonomous ground vehicles executing simulated military supply missions safely and effectively in a mock urban area” (DARPA press release)

Challenge completed November 2007

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60 mile urban area course, to be completed in less than 6 hours.Rules – following all traffic regulations, negotiating with other traffic and obstacles.E.g. maintaining precedence at 4 way stop intersection11 teams given development funding.

This challenge less physically demanding, but involved encounters with other vehicles.

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$2000,000 Winner: Tartan Racing (Carnegie Mellon University)Averaged 14 miles per hour throughout course.2nd: Stanford Racing (Stanford University).

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Next Grand ChallengeWhere do you think that robots could most usefully be employed?(I.e Where should funding be put?)What kind of robots do you think are likely to be developed?

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Swarm Robotics and applications

Swarm robotic principlesBiological inspiration from social insectsSimple autonomous agents

Decentralised local controlMinimal communication and representationReactive behaviour

Interaction with environment Emergence, situatedness, embodiment

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Advantages for applications

Cheap, expendable autonomous robotsAble to negotiate and exploit environment to achieve emergent cooperative solutions to practical problemsRedundancy and simplicity means robots can be added, or removed, without requiring recalibration or mission failure

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Possible application areas

Areas that are hostile or inaccessible to humanse.g. clearing up toxic waste or contaminated buildingse.g. mine fieldse.g. planetary exploratione.g. burning or collapsed buildingse.g. battlefield search for survivors

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Pheromone roboticsDavid Payton et al (HRL labs) (2004)Uses ‘virtual pheromones’Imagined scenario: rescue team enters unfamiliar building and needs to find survivorsSwarm of robots explores – one finds survivor and emits message.Message relayed locally among neighbouring robotsVirtual pheromone gradient propagated back to rescue workers.

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Pherobot

PalmV PDA used as main control computer

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Virtual pheromones implemented via infrared signals8 radially oriented directional infrared receivers and transmitters on each robotRobots can transmit and receive messages directionally relative to current orientationPheromone message also contains hop-count field which can be decremented as it is passed on – creating a pheromone gradient

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Augmented reality system: video camera on users head receives signals from robots and displays them as arrows

“world embedded computation”

No distinct step of map generation, but robots act as distributed set of processors embedded in environment.

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Robot-animal interactions Robot Sheep Dog projectVaughan, R., Sumpter, N., Henderson, J.,

Frost, A., and Cameron, S. (1999) Experiments in Automatic Flock Control. Robotics and Autonomous Systems, 31, 109-117.

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Minimal generalised model of underlying flock behaviourExperimental system: robot, workstation and video cameraRobot has top speed twice as fast as ducksPosition of robot and ducks determined by processing video imageBlob detection used for flock

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Flock-control algorithm – takes in vision data (positions of robot, flock and goal) and returns desired vehicle trajectory.Mimimal simulation of duck herding in circular arenaPotential field algorithm used to generate duck movement.Ducks are

attracted to each otherRepelled from each other if too closeRepelled from arena wallRepelled from robot

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- As an aside: Flocking

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Artificial Life examples

Craig Reynolds (1987) work on flocking behaviourBoids – virtual birds with basic flight capability

3 rules(i) collision avoidance – avoid collisions with nearby flock-mates(ii) velocity matching – attempt to match velocity with nearby flock-mates.(iii) flock centering – attempt to stay close to nearby flock-mates

Each boid is a basic unit that “sees” only its nearby flock-mates and “flies” according to the 3 rules.

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Result: boids flocked and flew as a cohesive group. When obstacles appeared in their way they spontaneously split into 2 subgroups, without central guidance, and rejoined after clearing obstruction.Boids algorithm: used to produce photorealistic imagery of bat swarms in “Batman returns”.

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Boids – illustrate basic principles of Alife systems

Large number of simple elemental unitsUnits interacting with nearby neighbours with no central controllerHigh-level emergent phenomena from low level interactions

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Returning to sheep dog…..Robot is

Attracted to flock with magnitude proportional to distance from goalRepelled from goal with constant magnitude

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Performance in real world

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Leurre project

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Leurre projectInsbot – mixed society of cockroaches and robotsAim – for Insbot to aggregate with cockroaches in shelter. Eventual aim to influence behaviour of cockroaches.

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Chemical sensing:Robot covered with medium impregnated with cockroach pheromonesBehaviour:

Controlled by fused combination of basic behavioursAggregation –more likely to stop if several cockroaches detected.

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Detection of shelters (ambient light), walls (IR sensors), robots (IR sensors and local communication) and cockroaches (IR sensors and linear camera)

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Summary and conclusionsReview of main characteristics of approachApplications

Unmanned vehicle (and DARPA grand challenge)Swarm robotics – Pheromone robotsRobot sheep dogInsbot and cockroachesIllustration of practical promise –

Keep it simple approach – minimal representationEmbodiment, interaction with environmentEmergence, autonomy