Robot IntelligenceRobot Intelligence

Kevin Warwick Kevin Warwick

Reactive Architectures I: Reactive Architectures I: SubsumptionSubsumption

• Perhaps the best known reactive architecture, Perhaps the best known reactive architecture, developed in the ‘80s by Rodney Brooks developed in the ‘80s by Rodney Brooks

• Each behaviour is defined in a layerEach behaviour is defined in a layer– takes sensory inputtakes sensory input– produces required robot motor outputproduces required robot motor output– each layer has a defined level of competence and each layer has a defined level of competence and

hence an associated priorityhence an associated priority– ExamplesExamples

• collision avoidance (low competence, high priority)collision avoidance (low competence, high priority)• path follow (higher competence, lower priority)path follow (higher competence, lower priority)• wander aimlesslywander aimlessly• build map (high competence, low priority)build map (high competence, low priority)• look for changeslook for changes

Reactive Architecture Reactive Architecture I:Subsumption I:Subsumption

• Each layer is integrated into a Each layer is integrated into a subsumption architecture wherebysubsumption architecture whereby– for each orthogonal mode there is only one for each orthogonal mode there is only one

actual output actual output •position of robot is orthogonal to (independent position of robot is orthogonal to (independent

of), say, position of a pan/tilt camera platformof), say, position of a pan/tilt camera platform

– A lower competence can always subsume (or A lower competence can always subsume (or suppress) the output from a higher suppress) the output from a higher competencecompetence

– ““Default behaviour” is always the lowest Default behaviour” is always the lowest competence onecompetence one

Reactive Architectures II: Motor Reactive Architectures II: Motor SchemaSchema

• Individual motor behaviours are defined based on Individual motor behaviours are defined based on sensory input (much the same as subsumption)sensory input (much the same as subsumption)

• Output of each schema is a velocity vector Output of each schema is a velocity vector representing direction and speedrepresenting direction and speed

• Difference with subsumption is that a number of Difference with subsumption is that a number of schema may be active at any one timeschema may be active at any one time– The emergent behaviour is a combination of groups of The emergent behaviour is a combination of groups of

motor schemamotor schema– Thus there is an element of co-operation between motor Thus there is an element of co-operation between motor

schemasschemas• Disadvantages of motor schema?Disadvantages of motor schema?

– How can groups of motor schema be combined, other How can groups of motor schema be combined, other than by the designer?than by the designer?

– How can changes in the active group be effected?How can changes in the active group be effected?• Arbitration of Behaviours can be carried out through Arbitration of Behaviours can be carried out through

sequencing.sequencing.

Reactive Architectures III: Ego Reactive Architectures III: Ego Behaviour Behaviour • Both subsumption and motor schema Both subsumption and motor schema

architectures rely on each behaviour operating architectures rely on each behaviour operating without any feedback on the emergent without any feedback on the emergent behaviour of the systembehaviour of the system

• Each behaviour is also fixed in terms of the Each behaviour is also fixed in terms of the input-to-output mappinginput-to-output mapping

• An alternative approach employs a strategy for An alternative approach employs a strategy for changing the way a behaviour contributes to changing the way a behaviour contributes to the emergent behaviour based on the emergent behaviour based on – knowledge of the emergent behaviour (feedback)knowledge of the emergent behaviour (feedback)– self-awareness of the behaviour itselfself-awareness of the behaviour itself

• This is effected by giving each behaviour an This is effected by giving each behaviour an EgoEgo

Ego BehaviourEgo Behaviour

• The Ego itself is here defined through a simple variable gain The Ego itself is here defined through a simple variable gain PD controller where the gains are updated using fuzzy logic to PD controller where the gains are updated using fuzzy logic to either either – strengthen the contribution of the behaviour orstrengthen the contribution of the behaviour or– withdraw the behaviour from contributingwithdraw the behaviour from contributing

Ego-Behaviour Experiments: Ego-Behaviour Experiments: 11• Two behaviours are Two behaviours are

presentpresent– Cs is a strong ego-Cs is a strong ego-

behaviour and wants behaviour and wants to get to –1.5to get to –1.5

– Cw is a weak ego-Cw is a weak ego-behaviour and wants behaviour and wants to get to +1to get to +1

– After 1 second Cw After 1 second Cw realises that it is not realises that it is not able to compete and able to compete and withdrawswithdraws

Ego-Behaviour Experiments:2Ego-Behaviour Experiments:2

• Three behaviours are Three behaviours are presentpresent– Cs is a strong ego-Cs is a strong ego-

behaviour and wants to behaviour and wants to get to –1.5get to –1.5

– Cm1 is a medium ego-Cm1 is a medium ego-behaviour and wants to behaviour and wants to get to +1get to +1

– Cm2 is a medium ego-Cm2 is a medium ego-behaviour and wants to behaviour and wants to get to +2get to +2

Ego-Behaviour Experiments:2 - Ego-Behaviour Experiments:2 - continuedcontinued

• Three behaviours are Three behaviours are presentpresent– After 0.6 seconds the After 0.6 seconds the

stronger ego-behaviour stronger ego-behaviour is overcome by both is overcome by both medium behaviours medium behaviours acting in co-operation. acting in co-operation.

– The emergent The emergent behaviour swings in behaviour swings in favour of Cm2 and Cm1 favour of Cm2 and Cm1 drops out after 1 drops out after 1 second.second.

Ego-Behaviour Experiments: Ego-Behaviour Experiments: 33• Tele-assisted viewing exampleTele-assisted viewing example• The “hot spot” is a camera view The “hot spot” is a camera view

centred on a tool rackcentred on a tool rack• In this scenario the operator In this scenario the operator

moves the slave manipulator moves the slave manipulator towards the tool racktowards the tool rack

• The emergent behaviour of the The emergent behaviour of the automated camera view tracks automated camera view tracks the end of the slave until it the end of the slave until it enters the hot spotenters the hot spot

• The ego-behaviour associated The ego-behaviour associated with fixating the camera on the with fixating the camera on the centre of the hot spot becomes centre of the hot spot becomes dominant, stabilising the dominant, stabilising the camera on the centrecamera on the centre

• After the slave has moved away After the slave has moved away from the hotspot the camera from the hotspot the camera resumes tracking of the slave resumes tracking of the slave tiptip

Evolutionary RoboticsEvolutionary Robotics

• Evolutionary Robotics falls under the Evolutionary Robotics falls under the category of artificial life.category of artificial life.

• Artificial Life is the study of “Life as it could Artificial Life is the study of “Life as it could be”be”

• Based on understanding the principles and Based on understanding the principles and simulating the mechanisms of real biological simulating the mechanisms of real biological life formslife forms

• Evolutionary robotics, as the name Evolutionary robotics, as the name suggests, borrows from our knowledge of suggests, borrows from our knowledge of the principles of biological evolution to the principles of biological evolution to evolve robot controllers, sensors and/or evolve robot controllers, sensors and/or physical morphology from the bottom up. physical morphology from the bottom up.

Artificial Evolution Artificial Evolution

• Extended Genetic Algorithms are used to Extended Genetic Algorithms are used to evolve Controllers, Bodies, Sensors and/or evolve Controllers, Bodies, Sensors and/or Actuators.Actuators.

• Simulation is used extensively to evaluate Simulation is used extensively to evaluate agent behaviours without damage to real agent behaviours without damage to real robots and to evaluate, in a reasonable robots and to evaluate, in a reasonable amount of time, the vast number of amount of time, the vast number of generations that evolution requiresgenerations that evolution requires

• Typically, only then is a final behaviour tested Typically, only then is a final behaviour tested on a real robot.on a real robot.

How and what to evolve?How and what to evolve?

• Highly recurrent free-form neural networks Highly recurrent free-form neural networks are usually used to control robot are usually used to control robot behaviours – these are suitable for behaviours – these are suitable for evolution due to their distributed structure.evolution due to their distributed structure.

• Typically, a fixed robot body is usedTypically, a fixed robot body is used

• However, the Genetic description can also However, the Genetic description can also define sensor morphology and complete define sensor morphology and complete body shape.body shape.

How a behaviour is evolvedHow a behaviour is evolved

• A task we wish to solve has to be definedA task we wish to solve has to be defined• A suitable simulation is required to test the A suitable simulation is required to test the

ability of agents to solve the given task ability of agents to solve the given task quantitatively.quantitatively.

• This quantitative measure or “fitness” is This quantitative measure or “fitness” is used by the Genetic Algorithm to produce used by the Genetic Algorithm to produce successive generations of agents until a successive generations of agents until a suitable level of proficiency has been suitable level of proficiency has been acquired.acquired.

• Then the proficient behaviour can be Then the proficient behaviour can be transferred to a real robot.transferred to a real robot.

A typical evolved “free-A typical evolved “free-form” neural network form” neural network controller.controller.

Artificial EvolutionArtificial Evolution

• Complex behaviours and structures Complex behaviours and structures can be evolved in simulation.can be evolved in simulation.

• Even for simple tasks evolution can Even for simple tasks evolution can produce surprisingly complex and produce surprisingly complex and life-like solutions.life-like solutions.

• If a suitable simulation is used these If a suitable simulation is used these behaviours and structures are behaviours and structures are transferable to real world robots.transferable to real world robots.

Robot Sensing – Key pointsRobot Sensing – Key points

• CostCost

• WeightWeight

• ReliabilityReliability

• FunctionalityFunctionality

• SimplicitySimplicity

• Power requirements/weightPower requirements/weight

• Computing requirements – on board?Computing requirements – on board?

• Application driven – what is required? Application driven – what is required?

VisionVision

• Is this needed?Is this needed?

• Can be expensive – Can be expensive – computationally/financiallycomputationally/financially

• Can take time Can take time

• Human-like – human-world?Human-like – human-world?

Machine VisionMachine Vision

• Image transformation – camera/CCD Image transformation – camera/CCD arrayarray

• Image analysis – filtering, edge Image analysis – filtering, edge detection, line finding – colour, texture?detection, line finding – colour, texture?

• Image understanding – AI methods, Image understanding – AI methods, segmentation, blocks world.segmentation, blocks world.

Range Finding/TriangulationRange Finding/Triangulation

• Passive – correspondence problemPassive – correspondence problem

• Active triangulation - Spot sensingActive triangulation - Spot sensing

• Time-of flight ranging – Sonar/LaserTime-of flight ranging – Sonar/Laser

Proximity SensingProximity Sensing

• Mechanical switchMechanical switch

• Inductive/Capacitive sensors – C = Inductive/Capacitive sensors – C = εεA/d – one plate on robot, one on A/d – one plate on robot, one on object – change in area object – change in area > change in > change in capacitancecapacitance

• Magnetic sensors – reed/HallMagnetic sensors – reed/Hall

• Optical position – phototransistor, Optical position – phototransistor, optical interrupter, optical reflectoroptical interrupter, optical reflector

Tactile SensingTactile Sensing

• Probably not necessary for a typical, Probably not necessary for a typical, industrial mobile robotindustrial mobile robot

• Needed when a robot performs Needed when a robot performs delicate assemblydelicate assembly

• Sense force in jointsSense force in joints

• Sense touchSense touch

• Sense slipSense slip

Robot IntelligenceRobot Intelligence

• Required intelligence will depend on Required intelligence will depend on sensor/actuator arrangementssensor/actuator arrangements

• Intellectual capabilities will depend Intellectual capabilities will depend on sensor/actuator capabilitieson sensor/actuator capabilities

• Sensors/actuators/brain(computer) Sensors/actuators/brain(computer) will all be different to human/animal will all be different to human/animal versionsversions

• RI is evolving at techno-rates not RI is evolving at techno-rates not biological ratesbiological rates

• So where will it be in 2035? So where will it be in 2035?