ROB 537: Learning-Based Control
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1 ROB 537: Learning-Based Control Week4,Lecture1 NeuralNetworksforControl Announcements: Backgroundpaperduetodayat6pm HW2Dueon10/23 MidtermExamon11/2 Reading:PapersonNeurocontrol Miikkulaininen Shepherd Problem Definitions DoweknowwhataregoodrobotacNons? - YES:Supervisedlearning Drivearoundwiththetraining on togenerateinput/outputpairs - NO:Unsupervisedlearning(criNcbasedlearning) Explore parametersnllyoufind rightbehavior Online/Offline? - Offline:train/searchforacompletesoluNonbeforeimplemenNng - Online:takeacNon,evaluate,takenextacNonetc.
2 Example: Robot Control Points of Interest Points of Interest Sensor Rover Sensor Robotsobserveenvironmentthroughsomesensors Sensorsareinputedintoaneuralnetwork OutputofneuralnetworkdeterminesdirecNon/velocityofrover Robot Control Sensor Inputs Desired Heading Autonomous Robot Heading
3 Robot Control Sensor Inputs Desired Heading Neuro Controller Steering Autonomous Robot Heading Robot Control Sensor Inputs Desired Heading Neuro Controller Steering Autonomous Robot Heading Neural Network Training Signal
4 Robot Control : Learn with a Teacher Sensor Inputs Desired Heading Neuro Controller Steering Autonomous Robot Heading Steering Error Robot Control : Learn without a Teacher Sensor Inputs Desired Heading Neuro Controller Steering Autonomous Robot Heading Robot Performance
5 Neural Networks for Nonlinear Control MoNvaNon: Controlasystemwithnonlineardynamics Robot Satellite Airvehicle Doweknowwhatthegoodcontrolstrategiesare? Yes: teach neuralnetworkthosestrategies DriveacarandrecordgooddriveracNonsforeachstate FlyahelicopterandrecordgoodpilotacNonsforeachstate No:haveaneuralnetworkdiscoverthosestrategies Letcardrivearoundandprovidefeedbackonperformance Neuro-Control with a teacher 1. IniNalizeaneuralnetwork 2. Letneuralnetworkpickheading 3. Computeheadingerrorusingteacher E 4. Useerrortoupdateneuralnetworkweights Δw = 5. Gotostep2
6 Unsupervised Learning Whatifwedon thaveateacher? Unsupervisedlearning:learningwithoutasetoflabeledexamples Eachinputresultsinanoutcome(measuredbyareward) Training:? Neuro-Control without a teacher 1. Att=0iniNalizeNneuralnetworks 2. Pickanetworkusingε greedyalg(ε=.1) 3. Randomlymodifynetworkparameters 4. UsenetworkonthisagentforTsteps 5. Evaluatenetworkperformance 6. Recinsertnetworkintopool 7. Removeworstnetworkfrompool 8. Gotostep2 R
7 Example: Quadrotor Control Benefitsofquadrotors: - Operateindangerous&challengingenvironments - OvercomeresourcelimitaNons - Maneuverabilitycoverairplanes - Mechanicalsimplicitycoverhelicopters Drawbacks: - Highlynonclineardynamics - UnintuiNvecontrol(difficultforahuman) - Stabilityproblems asopposedtoairplanes - Controlproblems asopposedtohelicopters Background Quadrotor Control
8 Background Quadrotor Control Background Quadrotor Control
9 Background Quadrotor Control Background Quadrotor Control
10 Background Quadrotor Control Background Quadrotor Control
11 Background Quadrotor Control Background Quadrotor Control
12 Background Quadrotor Control Background Quadrotor Control
13 Background Quadrotor Control Background Quadrotor Control
14 Controller Formulation Breakproblemdownintosolvableunits - TopLevel:PosiNoncontrollercforwaypointnavigaNon - MiddleLevel:Aitudecontrollercformaintainingstability - Lowlevel:Pitch/Yawcontroller forspecificrotormovement - Allfollowingresultsfrom: J.ShepherdIIIandK.Tumer,Robust'Neuro+Control'for'A'Micro'Quadrotor.InProceedings+of+ the+gene0c+and+evolu0onary+computa0on+conference,pp ,Portland,OR,July Position Controller Formulation
15 Position Controller Formulation Position Controller Training Fitness based on distance traveled and distance to goal: Stability criteria - Erratic motion resulted in a fitness set to iterations to train : - Select - Mutate - Evaluate - Evaluate position controller multiple grid points - Run with neuro-controller for 10 seconds
16 Attitude Controller Formulation Attitude Controller Formulation
17 Attitude Controller Formulation Attitude Controller - Two Step Training Step1:Supervised - PopulaNonof100controllers - Controlquadrotorfor10s - FitnessbasedonmatchingPIDresults - Runfor3000iteraNons Step2:Unsupervised - CreatenewpopulaNonfrombestcontrollersfromstep1 - Add20%weightmutaNon - Fitnessbaseddirectlyonachievedangle - Runforanother1000iteraNons
18 Experiments 1. NavigaNon - Setofwaypointsgiven,controllerperformancemeasured 2. Robustnesstodisturbances - ModeledasadiscretechangeinthecramscurrentorientaNon Suddenat30 pitch - Timetorecover,andrangeofabilitytorecovercompared 3. Robustnesstonoise - Randomsensorandactuatornoiseadded. - Averagedistancecramtraveledaroundthedesiredholdpointcompared 4. Robustnesstodesignparameters - Cramphysicalparameters(size,weight,thrust&dragcoefficients) - Timetoperformmovecompared(mostlybinaryresult). Navigation Results
19 2. Robustness to Disturbances 3. Robustness to Sensor/Actuator Noise
20 4. Robustness to Design Parameters Conclusions Controller Development/Performance DevelopedahierarchyofadapNvecontrollers - Dividedproblem - AppliedrelevantmodelinformaNon ProducedQuadrotorcontrolthatOvercameDisturbances - Upto180 flip HandledsensorandactuatorNoise - 4NmesbeqerthanPIDforsensornoise - 8NmesbeqerthanPIDforactuatornoise ProvidedinsensiNvitytoDesignParameter - Mass&Thrustchangesof±30%
21 Quadrotor Control: Demo Quadrotor Recovery: Demo
22 Learning in the Real World Input Agent' AcNon Reward' Input Agent' Intended AcNon Noise' Actual AcNon Actual' Reward' RewardReceivedbyAgent Noise' Human in the Loop: Suggestion Agents Input Agent' Intended AcNon Noise' Agent AcNon Filter' Actual AcNon Actual' Reward' RewardReceivedbyAgent Noise'
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