A1257278321pBoXr - PowerPoint PPT Presentation

Title: A1257278321pBoXr


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Closed Loop Temporal Sequence Learning Learning
to act in response to sequences of sensor events
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Overview over different methods
You are here !
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Natural Temporal Sequences in life and in
control situations

• Real Life
• Heat radiation predicts pain when touching a hot
  surface.
• Sound of a prey may precede its smell will
  precede its taste.
• Control
• Force precedes position change.
• Electrical (disturbance) pulse may precede change
  in a controlled plant.
• Psychological
  Experiment
• Pavlovian and/or Operant Conditioning Bell
  precedes Food.

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What we had so far !
Early Bell
w
S
Late Food
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conditioned Input
This is an open-loop system
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Closed loop
Behaving
Sensing
Adaptable Neuron
Env.
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How to assure behavioral learning convergence ??
This is achieved by starting with a stable
reflex-like action and learning to supercede it
by an anticipatory action.
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Robot Application
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Reflex Only (Compare to an electronic closed
loop controller!)
Think of a Thermostat !
This structure assures initial (behavioral)
stability (homeostasis)
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Retraction reflex
Bump
The Basic Control Structure Schematic diagram of
A pure reflex loop
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Robot Application
Learning Goal Correlate the vision signals with
the touch signals and navigate without collisions.
Initially built-in behavior Retraction reaction
whenever an obstacle is touched.
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Lets look at the Reflex first
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This is an open-loop system
The T represents the temporal delay between
vision and bump.
Closed Loop TS Learning
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Lets see how learning continues
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Antomically this loop still exists but ideally it
should never be active again !
This is the system after learning
Closed Loop TS Learning
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What has the learning done ?
Elimination of the late reflex input corresponds
mathematically to the condition X00
This was the major condition of convergence for
the ISO/ICO rules. (The other one was T0).
Whats interesting about this is that the
learning-induced behavior self-generates this
condition. This is non-trivial as it assures the
synaptic weight AND behavior stabilize at exactly
the same moment in time.
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The inner pathway has now become a
pure feed-forward path
What has happened in engineering terms?
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Formally
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The Learner has learned the inverse transfer
function of the world and can compensate the
disturbance therefore at the summation node!
-e-sT
0
e-sT
P1
D
Phase Shift
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The out path has, through learning, built a
Forward Model of the inner path. This is some
kind of a holy grail for an engineer
called Model Free Feed-Forward Compensation
For example If you want to keep the temperature
in an outdoors container constant, you can employ
a thermostat. You may however also first measure
how the sun warms the liquid in the container,
relating outside temperature to inside
temperature (Eichkurve). From this curve you
can extract a control signal for the cooling of
the liquid and react BEFORE the liquid inside
starts warming up. As you react BEFORE, this is
called Feed-Forward-Compensation (FFC). As you
have used a curve, have performed Model-Based
FFC. Our robot learns the model, hence we do
Model Free FFC.
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Example of ISO instability as compared to ICO
stability !
Remember the Auto-Correlation Instability of ISO??
dw1
m u1 u0
ICO
dt
dw1
m u1 v
ISO
dt
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Old ISO-Learning
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New ICO-Learning
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Full compensation
Over compensation
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Statistical Analysis Conventional differential
hebbian learning
One-shot learning (some instances)
Highly unstable
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Statistical Analysis Input Correlation Learning
One-shot learning (common)
Stable in a wide domain
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Two more applications RunBot With different
convergent conditions!
Walking is a very difficult problem as such and
requires a good, multi-layered control structure
only on top of which learning can take place.
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An excursion into Multi-Layered Neural Network
Control

• Some statements
• Animals are behaving agents. They receive sensor
  inputs from MANY sensors and they have to control
  the reaction of MANY muscles.
• This is a terrific problem and solved by out
  brain through many control layers.
• Attempts to achieve this with robots have so far
  been not very successful as network control is
  somewhat fuzzy and unpredictable.

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Adaptive Control during Waliking Three Loops
Step control Terrain control
Central Control (Brain)
Ground- contact
Spinal Cord (Oscillations )
Motorneurons Sensors (Reflex generation)
Muscles Skeleton (Biomechanics)
Muscle length
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Self-Stabilization by passive Biomechanical
Properties (Pendelum)
Muscles Skeleton (Biomechanics)
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Passive Walking Properties
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RunBots Network of the lowest loop
Motor neurons Sensors (Reflex generation)
Muscles Skeleton (Biomechanics)
Muscle length
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Leg Control of RunBot Reflexive Control (cf.
Cruse)
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Instantaneous Parameter Switching
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Body (UBC) Control of RunBot
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Long-Loop Reflex of one of the upper loops
Terrain control
Central Control (Brain)
Before or during a fall Leaning forward of
rump and arms
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Long-Loop Reflexe der obersten Schleife
Terrain control
Central Control (Brain)
Forward leaning UBC
Backward leaning UBC
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The Leg Control as Target of the Learning
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Learning in RunBot
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RunBot Learning to climb a slope
Spektrum der Wissenschaft, Sept. 06
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Human walking versus machine walking
ASIMO by Honda
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RunBots Joints
Ramp
Lower floor
Upper floor
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Change of Gait when walking upwards
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Learning relevant parameters
Too late
Early enough
X
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A different Learning Control Circuitry
AL, (AR) Stretch receptor for anterior angle of
left (right) hip GL, (GR) Sensor neuron for
ground contact of left (right) foot EI (FI)
Extensor (Flexor) reflex inter-neuron, EM (FM)
Extensor (Flexor) reflex motor-neuron ES (FS)
Extensor (Flexor) reflex sensor neuron
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Motor Neuron Signal Structure (spike rate)
Right Leg
Left Leg
x0
x1
Learning reverses pulse sequence ? Stability of
STDP
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Motor Neuron Signal Structure (spike rate)
Right Leg
Left Leg
x0
x1
Learning reverses pulse sequence ? Stability of
STDP
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The actual Experiment
Note Here our convergence condition has been
T0 (oscillatory!) and NOT as usual X00
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Speed Change by Learning
Note the Oscillations !
T 0 !
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dw1
m u1 u0
dt
dw1
m u1 v
dt
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Driving School Learn to follow a track and
develop RFs in a closed loop behavioral context
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Differential Hebbian learning used for STDP
ISO-learning for temporal sequence
learning (Porr and Wörgötter, 2003)
??
T
Central Features Filtering
of all inputs with low-pass filters
(membrane property) Reproduces asymmetric
weight change curve
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Modelling STDP (plasticity) and using this
approach (in a simplified way) to control
behavioral learning.
An ecological approach to some aspects
of theoretical neuroscience
Differential Hebbian Learning rule
Transfer of a computational neuroscience approach
to a technical domain.
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Pavlov in real life !
Adding a secondary loop
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What has happened during learning to the system ?
The primary reflex re-action has effectively been
eliminated and replaced by an anticipatory action