Robot Simulator of the Robotics Group for Self-Organization of Control: invertmotorbigmodel.h Source File

# invertmotorbigmodel.h

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    00001 /***************************************************************************
    00002  *   Copyright (C) 2005-2011 by                                            *
    00003  *    Georg Martius  <georg dot martius at web dot de>                     *
    00004  *    Frank Hesse    <frank at nld dot ds dot mpg dot de>                  *
    00005  *    Ralf Der       <ralfder at mis dot mpg dot de>                       *
    00006  *                                                                         *
    00007  *   ANY COMMERCIAL USE FORBIDDEN!                                         *
    00008  *   LICENSE:                                                              *
    00009  *   This work is licensed under the Creative Commons                      *
    00010  *   Attribution-NonCommercial-ShareAlike 2.5 License. To view a copy of   *
    00011  *   this license, visit http://creativecommons.org/licenses/by-nc-sa/2.5/ *
    00012  *   or send a letter to Creative Commons, 543 Howard Street, 5th Floor,   *
    00013  *   San Francisco, California, 94105, USA.                                *
    00014  *                                                                         *
    00015  *   This program is distributed in the hope that it will be useful,       *
    00016  *   but WITHOUT ANY WARRANTY; without even the implied warranty of        *
    00017  *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.                  *
    00018  *                                                                         *
    00019  ***************************************************************************/
    00020 #ifndef __INVERTMOTORBIGMODEL_H
    00021 #define __INVERTMOTORBIGMODEL_H
    00022
    00023 #include "invertmotorcontroller.h"
    00024
    00025 #include <assert.h>
    00026 #include <cmath>
    00027
    00028 #include "matrix.h"
    00029 #include "noisegenerator.h"
    00030 #include "invertablemodel.h"
    00031
    00032 typedef struct InvertMotorBigModelConf {
    00033   int buffersize;  ///< buffersize size of the time-buffer for x,y,eta
    00034   double cInit;    ///< cInit size of the C matrix to initialised with.
    00035   double cNonDiag; ///< cNonDiag is the size of the nondiagonal elements in respect to the diagonal (cInit) ones
    00036   bool modelInit;  ///< size of the unit-map strenght of the model
    00037   bool useS;    ///< useS decides whether to use the S matrix in addition to the A matrix
    00038   bool someInternalParams;  ///< someInternalParams if true only some internal parameters are exported, otherwise all
    00039
    00040   double modelCompliant; ///< learning factor for model (or sensor) compliant learning
    00041
    00042   InvertableModel* model;   ///< model used as world model
    00043 } InvertMotorBigModelConf;
    00044
    00045 /**
    00046  * class for robot controller is based on InvertMotorNStep
    00047  *
    00048  * - direct inversion
    00049  *
    00050  * - motor space
    00051  *
    00052  * - multilayer,nonlinear model
    00053  */
    00054 class InvertMotorBigModel : public InvertMotorController {
    00055
    00056 public:
    00057   InvertMotorBigModel(const InvertMotorBigModelConf& conf = getDefaultConf());
    00058   virtual void init(int sensornumber, int motornumber, RandGen* randGen = 0);
    00059
    00060   virtual ~InvertMotorBigModel();
    00061
    00062   /// returns the number of sensors the controller was initialised with or 0 if not initialised
    00063   virtual int getSensorNumber() const { return number_sensors; }
    00064   /// returns the mumber of motors the controller was initialised with or 0 if not initialised
    00065   virtual int getMotorNumber() const  { return number_motors; }
    00066
    00067   /// performs one step (includes learning).
    00068   /// Calulates motor commands from sensor inputs.
    00069   virtual void step(const sensor* , int number_sensors, motor* , int number_motors);
    00070
    00071   /// performs one step without learning. Calulates motor commands from sensor inputs.
    00072   virtual void stepNoLearning(const sensor* , int number_sensors,
    00073                               motor* , int number_motors);
    00074
    00075
    00076   /**************  STOREABLE **********************************/
    00077   /** stores the controller values to a given file. */
    00078   virtual bool store(FILE* f) const;
    00079   /** loads the controller values from a given file. */
    00080   virtual bool restore(FILE* f);
    00081
    00082   /************** INSPECTABLE ********************************/
    00083   virtual iparamkeylist getInternalParamNames() const;
    00084   virtual iparamvallist getInternalParams() const;
    00085   virtual ilayerlist getStructuralLayers() const;
    00086   virtual iconnectionlist getStructuralConnections() const;
    00087
    00088   /**** TEACHING ****/
    00089   /** The given motor teaching signal is used for this timestep.
    00090       It is used as a feed forward teaching signal for the controller.
    00091       Please note, that the teaching signal has to be given each timestep
    00092        for a continuous teaching process.
    00093    */
    00094   virtual void setMotorTeachingSignal(const motor* teaching, int len);
    00095
    00096   /** The given sensor teaching signal (distal learning) is used for this timestep.
    00097       First the belonging motor teachung signal is calculated by the inverse model.
    00098       See setMotorTeachingSignal
    00099    */
    00100   virtual void setSensorTeachingSignal(const sensor* teaching, int len);
    00101
    00102
    00103   static InvertMotorBigModelConf getDefaultConf(){
    00104     InvertMotorBigModelConf c;
    00105     c.buffersize = 50;
    00106     c.cInit = 1.0;
    00107     c.cNonDiag = 0;
    00108     c.modelInit  = 1.0;
    00109     c.someInternalParams = true;
    00110     c.useS = false;
    00111     c.modelCompliant = 0;
    00112     c.model = 0;
    00113     return c;
    00114   }
    00115
    00116   void getLastMotors(motor* motors, int len);
    00117
    00118 protected:
    00119   unsigned short number_sensors;
    00120   unsigned short number_motors;
    00121
    00122   matrix::Matrix A; ///< Model Matrix (motors to sensors)
    00123   matrix::Matrix S; ///< additional Model Matrix (sensors derivatives to sensors)
    00124   matrix::Matrix C; ///< Controller Matrix
    00125   matrix::Matrix H; ///< Controller Bias
    00126   NoiseGenerator* BNoiseGen; ///< Noisegenerator for noisy bias
    00127   matrix::Matrix R; ///< C*A
    00128   matrix::Matrix SmallID; ///< small identity matrix in the dimension of R
    00129   matrix::Matrix xsi; ///< current output error
    00130   double xsi_norm; ///< norm of matrix
    00131   double xsi_norm_avg; ///< average norm of xsi (used to define whether Modell learns)
    00132   double pain;         ///< if the modelling error (xsi) is too high we have a pain signal
    00133   matrix::Matrix* x_buffer;
    00134   matrix::Matrix* y_buffer;
    00135   matrix::Matrix* eta_buffer;
    00136   matrix::Matrix zero_eta; // zero initialised eta
    00137   matrix::Matrix x_smooth;
    00138   //   matrix::Matrix z; ///< membrane potential
    00139   matrix::Matrix y_teaching; ///< teaching motor signal
    00140   bool useTeaching; ///< flag whether there is an actual teachning signal or not
    00141   int t_rand; ///< initial random time to avoid syncronous management of all controllers
    00142
    00143
    00144   int managementInterval; ///< interval between subsequent management function calls
    00145   paramval inhibition; ///< inhibition strength for sparce kwta strategy (is scaled with epsC)
    00146   paramval kwta;       ///< (int) number of synapses that get strengthend
    00147   paramval limitRF;    ///< (int) receptive field of motor neurons (number of offcenter sensors) if null then no limitation. Mutual exclusive with inhibition
    00148   paramval dampS;     ///< damping of S matrix
    00149
    00150   InvertMotorBigModelConf conf;
    00151
    00152   /// puts the sensors in the ringbuffer, generate controller values and put them in the
    00153   //  ringbuffer as well
    00154   virtual void fillBuffersAndControl(const sensor* x_, int number_sensors,
    00155                              motor* y_, int number_motors);
    00156
    00157   /// calculates the first shift into the motor space useing delayed motor values.
    00158   //  @param delay 0 for no delay and n>0 for n timesteps delay in the time loop
    00159   virtual void calcEtaAndBufferIt(int delay);
    00160
    00161   /// learn H,C with motors y and corresponding sensors x
    00162   virtual void learnController();
    00163
    00164   /// calculates the Update for C and H
    00165   // @param y_delay timesteps to delay the y-values.  (usually 0)
    00166   //  Please note that the delayed values are NOT used for the error calculation
    00167   //  (this is done in calcXsi())
    00168   virtual void calcCandHUpdates(matrix::Matrix& C_update, matrix::Matrix& H_update, int y_delay);
    00169
    00170   /// updates the matrix C and H
    00171   virtual void updateCandH(const matrix::Matrix& C_update, const matrix::Matrix& H_update, double squashSize);
    00172
    00173   /// learn A, (and S) using motors y and corresponding sensors x
    00174   //  @param delay 0 for no delay and n>0 for n timesteps delay in the time loop
    00175   virtual void learnModel(int delay);
    00176
    00177   /// handles inhibition damping etc.
    00178   virtual void management();
    00179
    00180   /// returns controller output for given sensor values
    00181   virtual matrix::Matrix calculateControllerValues(const matrix::Matrix& x_smooth);
    00182
    00183   /** Calculates first and second derivative and returns both in on matrix (above).
    00184       We use simple discrete approximations:
    00185       \f[ f'(x) = (f(x) - f(x-1)) / 2 \f]
    00186       \f[ f''(x) = f(x) - 2f(x-1) + f(x-2) \f]
    00187       where we have to go into the past because we do not have f(x+1). The scaling can be neglegted.
    00188   */
    00189   matrix::Matrix calcDerivatives(const matrix::Matrix* buffer, int delay);
    00190
    00191 public:
    00192   /** k-winner take all inhibition for synapses. k largest synapses are strengthed and the rest are inhibited.
    00193       strong synapes are scaled by 1+(damping/k) and weak synapses are scaled by 1-(damping/(n-k)) where n is the
    00194       number of synapes
    00195       @param weightmatrix reference to weight matrix. Synapses for a neuron are in one row.
    00196              The inhibition is done for all rows independently
    00197       @param k number of synapes to strengthen
    00198       @param damping strength of supression and exitation (typically 0.001)
    00199    */
    00200   void kwtaInhibition(matrix::Matrix& weightmatrix, unsigned int k, double damping);
    00201
    00202   /** sets all connections to zero which are further away then rfSize.
    00203       If rfSize == 1 then only main diagonal is left.
    00204       If rfSize = 2: main diagonal and upper and lower side diagonal are kept and so on and so forth.
    00205    */
    00206   void limitC(matrix::Matrix& weightmatrix, unsigned int rfSize);
    00207
    00208 };
    00209
    00210 #endif