SeMoX Class Reference

This controller follows the prinziple of homeokinesis and implements the extensions described in the thesis of Georg Martius 2009, University Goettingen: Goal-Oriented Control of Self-organizing Behavior in Autonomous Robots. More...

#include <semox.h>

Inherits HomeokinBase, and Teachable.

Collaboration diagram for SeMoX:

[legend]

List of all members.

Public Member Functions
	SeMoX (const SeMoXConf &conf=getDefaultConf())
virtual void	init (int sensornumber, int motornumber, RandGen *randGen=0)
	initialisation of the controller with the given sensor/ motornumber Must be called before use.
virtual	~SeMoX ()
virtual int	getSensorNumber () const
	returns the number of sensors the controller was initialised with or 0 if not initialised
virtual int	getMotorNumber () const
	returns the mumber of motors the controller was initialised with or 0 if not initialised
virtual void	step (const sensor , int number_sensors, motor , int number_motors)
	performs one step (includes learning).
virtual void	stepNoLearning (const sensor , int number_sensors, motor , int number_motors)
	performs one step without learning. Calulates motor commands from sensor inputs.
virtual bool	store (FILE *f) const
	stores the controller values to a given file.
virtual bool	restore (FILE *f)
	loads the controller values from a given file.
virtual std::list< ILayer >	getStructuralLayers () const
	Specifies which parameter vector forms a structural layer (in terms of a neural network) The ordering is important.
virtual std::list< IConnection >	getStructuralConnections () const
	Specifies which parameter matrix forms a connection between layers (in terms of a neural network) The orderning is not important.
virtual void	setMotorTeaching (const matrix::Matrix &teaching)
	The given motor teaching signal is used for this timestep.
virtual void	setSensorTeaching (const matrix::Matrix &teaching)
	The given sensor teaching signal (distal learning) is used for this timestep.
virtual matrix::Matrix	getLastMotorValues ()
	returns the last motor values (useful for cross motor coupling)
virtual matrix::Matrix	getLastSensorValues ()
	returns the last sensor values (useful for cross sensor coupling)
Static Public Member Functions
static SeMoXConf	getDefaultConf ()
	returns the default configuration
Protected Member Functions
virtual void	fillBuffersAndControl (const sensor x_, int number_sensors, motor y_, int number_motors)
	puts the sensors in the ringbuffer, generate controller values and put them in the
virtual void	calcXsi (int delay)
	calculates xsi for the current time step using the delayed y values
virtual void	learnController ()
	learn H,C with motors y and corresponding sensors x
virtual void	learnModel (int delay)
	learn A, (and S) using motors y and corresponding sensors x
virtual matrix::Matrix	model (const matrix::Matrix *x_buffer, int delay, const matrix::Matrix &y)
	calculates the predicted sensor values
virtual void	management ()
	handles inhibition damping etc.
virtual matrix::Matrix	calculateControllerValues (const matrix::Matrix &x_smooth)
	returns controller output for given sensor values
Static Protected Member Functions
static double	regularizedInverse (double v)
Protected Attributes
unsigned short	number_sensors
unsigned short	number_motors
matrix::Matrix	A
	Model Matrix (motors to sensors).
matrix::Matrix	S
	additional Model Matrix (sensors derivatives to sensors)
matrix::Matrix	C
	Controller Matrix.
matrix::Matrix	H
	Controller Bias.
matrix::Matrix	B
	Model Bias.
matrix::Matrix	R
	C*A.
matrix::Matrix	SmallID
	small identity matrix in the dimension of R
matrix::Matrix	v
	shift
matrix::Matrix	xsi
	current output error
NoiseGenerator *	BNoiseGen
	Noisegenerator for noisy bias.
paramval	modelNoise
	strength of noisy bias
double	xsi_norm
	norm of matrix
double	xsi_norm_avg
	average norm of xsi (used to define whether Modell learns)
double	pain
	if the modelling error (xsi) is too high we have a pain signal
matrix::Matrix *	x_buffer
matrix::Matrix *	x_c_buffer
	buffer for sensors with context sensors
matrix::Matrix *	y_buffer
matrix::Matrix	y_teaching
	motor teaching signal
paramval	gamma_cont
	parameter to include contiuity in motor values (avoid high frequencies)
paramval	gamma_teach
	strength of teaching
paramval	discountS
	discount strength for hierachical model
paramval	dampModel
	damping of A and S matrices
paramval	dampController
	damping of C matrix
SeMoXConf	conf
bool	intern_useTeaching
	flag whether there is an actual teachning signal or not
int	t_rand
	initial random time to avoid syncronous management of all controllers
int	managementInterval
	interval between subsequent management function calls
parambool	_modelExt_copy
	copy of modelExtension variable (to achieve readonly)
Friends
class	ThisSim

Detailed Description

This controller follows the prinziple of homeokinesis and implements the extensions described in the thesis of Georg Martius 2009, University Goettingen: Goal-Oriented Control of Self-organizing Behavior in Autonomous Robots.

This class also implements part of the guided self-organization

Name: SElf-organizing MOtor space eXtended

Main characteristics: Motor Space, Extended World model, Continuity, Teaching interface

Constructor & Destructor Documentation

SeMoX ( const SeMoXConf & conf = getDefaultConf() )

~SeMoX ( ) [virtual]

Member Function Documentation

Matrix calculateControllerValues ( const matrix::Matrix & x_smooth ) [protected, virtual]

returns controller output for given sensor values

calculate controller outputs

Parameters:

x_smooth

smoothed sensors Matrix(number_channels,1)

void calcXsi ( int delay ) [protected, virtual]

calculates xsi for the current time step using the delayed y values

void fillBuffersAndControl	(	const sensor *	x_,
		int	number_sensors,
		motor *	y_,
		int	number_motors
	)			`[protected, virtual]`

puts the sensors in the ringbuffer, generate controller values and put them in the

static SeMoXConf getDefaultConf ( ) [inline, static]

returns the default configuration

matrix::Matrix getLastMotorValues ( ) [virtual]

returns the last motor values (useful for cross motor coupling)

Implements Teachable.

matrix::Matrix getLastSensorValues ( ) [virtual]

returns the last sensor values (useful for cross sensor coupling)

Implements Teachable.

virtual int getMotorNumber ( ) const [inline, virtual]

returns the mumber of motors the controller was initialised with or 0 if not initialised

Implements AbstractController.

virtual int getSensorNumber ( ) const [inline, virtual]

returns the number of sensors the controller was initialised with or 0 if not initialised

Implements AbstractController.

list< Inspectable::IConnection > getStructuralConnections ( ) const [virtual]

Specifies which parameter matrix forms a connection between layers (in terms of a neural network) The orderning is not important.

Returns:: : list of layer names with dimension

Reimplemented from Inspectable.

list< Inspectable::ILayer > getStructuralLayers ( ) const [virtual]

Specifies which parameter vector forms a structural layer (in terms of a neural network) The ordering is important.

The first entry is the input layer and so on.

Returns:: : list of layer names with dimension

Reimplemented from Inspectable.

void init	(	int	sensornumber,
		int	motornumber,
		RandGen *	randGen = `0`
	)			`[virtual]`

initialisation of the controller with the given sensor/ motornumber Must be called before use.

The random generator is optional.

Implements AbstractController.

void learnController ( ) [protected, virtual]

learn H,C with motors y and corresponding sensors x

learn controller paramters C and H

void learnModel ( int delay ) [protected, virtual]

learn A, (and S) using motors y and corresponding sensors x

void management ( ) [protected, virtual]

handles inhibition damping etc.

Matrix model	(	const matrix::Matrix *	x_buffer,
		int	delay,
		const matrix::Matrix &	y
	)			`[protected, virtual]`

calculates the predicted sensor values

static double regularizedInverse ( double v ) [static, protected]

bool restore ( FILE * f ) [virtual]

loads the controller values from a given file.

Implements Storeable.

void setMotorTeaching ( const matrix::Matrix & teaching ) [virtual]

The given motor teaching signal is used for this timestep.

It is used as a feed forward teaching signal for the controller. Please note, that the teaching signal has to be given each timestep for a continuous teaching process.

Parameters:

teaching,:

matrix with dimensions (motornumber,1)

Implements Teachable.

void setSensorTeaching ( const matrix::Matrix & teaching ) [virtual]

The given sensor teaching signal (distal learning) is used for this timestep.

The belonging motor teachung signal is calculated by the inverse model. See setMotorTeaching

Parameters:

teaching,:

matrix with dimensions (motorsensors,1)

Implements Teachable.