TCG IX Mobile Robotics

albanianboneyardΤεχνίτη Νοημοσύνη και Ρομποτική

2 Νοε 2013 (πριν από 3 χρόνια και 10 μήνες)

92 εμφανίσεις


Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA


BECCA

A Brain Emulating

Cognition and Control
Architecture



Brandon Rohrer


Intelligent Systems, Robotics, and Cybernetics Group


Sandia National Laboratories


2008 AAAI, BICA Workshop


Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

Biases: What “biologically
-
inspired” means to me


Self preservation and perpetuation is
the

implicit goal
of biological systems.


If it is not, they don’t last very long


Implies
real
-
world interaction



There is no
a priori

model of the environment


All categories and concepts are acquired


Human cognition violates rationality, formal logics,
optimality, universal concept definitions, strict
ontologies, and strict perceptual categorizations


A BICA should not presuppose any of these


Broad functional equivalence to humans is the goal of
BICA research


All empirical data (neuroanatomy, cognitive
neuroscience, experimental psychology, etc.) are rich
sources for clues about how this might be achieved


Existing theories and constructs describing empirical
data should be used with caution


Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

BECCA

A Brain Emulating

Cognition and Control Architecture


Brandon Rohrer







BECCA is a biomimetic approach to
achieving human
-
like reasoning, perception,
learning, and movement control in machines


It has two core algorithms


S
-
Learning
: A solution to some Dynamic
Reinforcement Learning problems




Context
-
Based Similarity
: A solution to the
problem of semantically clustering symbols


Given an ordered set of symbols, determine the
semantic distance (similarity) between any two



Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

BECCA Operational Diagram


Sensor and control information are handled symbolically



are passed in “episodic” fashion, quantized in time,


are discretized in magnitude,


are treated categorically


extrapolation and interpolation does not occur explicitly.


Allows very general application


Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

S
-
Learning Algorithm:

A solution to some Dynamic RL problems


S
-
Learning (
sequence

learning)


records observed sequences and uses them


to make predictions about future events


and control decisions


Algorithm description


1. Learning


At each iteration, record the longest novel sequence


Reinforce the longest familiar sequence


2. Prediction


Find sequences that begin with the most recent state(s)


The remainder of those sequences constitute predictions


3. Control


Evaluate each prediction for potential reward


Select a prediction to re
-
enact


Execute the same action sequence


Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

S
-
Learning: Rotary robot


Simulation of a one degree
-
of
-
freedom rotary pointer robot,


Sensor quantized in 10º increments


Movement by 10º increments


S
-
Learning demonstrated the ability
to learn and predict hard
nonlinearities


S
-
Learning performed optimally
even in the presence of


Scrambled sensor conditions


Gain reversals


Stochastic movement errors


Random time delays


No explicit model of the system was
provided

its workings were
discovered by S
-
Learning


Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

S
-
Learning: Reaching simulation


Two degree
-
of
-
freedom robot
reaching simulation


Approximately human parameters
used for inertia. movement
characteristics, and sensing
capabilities


Robot learned to reach a fixed target
at an arbitrary position in the plane


Demonstrated
generalization


Learning in one task was applied to
a second task


This, despite the complete
separation of the sensory
representations of the two tasks



No explicit model of the system was
provided

its workings were
discovered by S
-
Learning

Early

Mature

Intermediate

Click to

play videos


Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

S
-
Learning: Grasping simulation


Three degree
-
of
-
freedom
robot grasping simulation
with rich sensors:


Coarse vision


Coarse position


Contact pressure


Robot learned to reach a
fixed target at an given
position in the plane


Learned to coordinate grasp
with motion to grab target


No explicit model of the
system was provided

its
workings were discovered
by S
-
Learning

Click to

play video


Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

Context
-
Based Similarity (CBS):

A solution to semantic symbol clustering


Underlying concept: Symbols are similar if they occur
in identical contexts.


Context

refers to the symbols that temporally precede
and follow the symbol of interest.


CBS finds the word “great” and the phrase “very
large” to be similar because they are preceded and
followed by the same symbols (words)

they have
identical contexts



Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

CBS: Natural Language Processing


After reading 25 million words, CBS performed
synonym extraction (finding sets of words that
occurred in contexts identical to a seed word)


No part
-
of
-
speech tags were given


In fact, CBS did not do anything that was specific to
English, text, or language in general. It would have
handled encoder readings, force data, or image
properties the same way.


Plausible synonym groups were created


Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

CBS: Perception and concept formation


Formulating the concept of the object <vehicle> by
compiling specific examples


Repeated sequences of 1) a static video background,
2) a dynamic video component created by a moving
vehicle, 3) detected motion, and 4) detected sound
allow the semantic cluster or
concept

of <vehicle> to
be formed.


Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

BECCA Operational Diagram


When combined, S
-
Learning and Context
-
Based Similarity
provides BECCA’s full capabilities.


the ability to manage the curse of dimensionality in complex
systems


concept generation


hierarchical processing for additional abstraction and
sophistication


Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

Biological Inspiration


Discrete motor actions

observed in slow movements
(Vallbo and Wessberg 1993), infants’ movements
(von Hofsten 1991) and repetitive movements
(Woodworth 1899)


Discrete sensory states

proposed by James (1890);
evidence seen in carefully structured psychophysical
experiments (Gho and Varela 1988, VanRullen and
Koch 2003)


Periodic processing

and storage suggested by
thalamcortical loops (Rodriguez, Whitson, and
Granger 2004)


Sequence storage

transition from hippocampus to
parahippocampus to cortex described (Eichenbaum et
al. 1999)


Possible mechanism for
classifying

and
selecting
between

sequences

in cerebellar
-
cortical
-
basal
-
ganglionic loops (Houk 2005)



Overview

S
-
Learning

Rotary Robot

Reaching

Grasping

Context
-
Based
Similarity

Natural Language
Processing

Perception

Bio
-
Inspiration

Applications


BECCA

Helping robots handle

real
-
world interactions

Potential problem spaces for BECCA
-
driven robots:



Learn to manipulate unfamiliar objects


Learn complex perceptuo
-
motor tasks


Create high
-
level abstractions


Make predictions about future events


Use reasoning to achieve goals


Solve poorly
-
posed problems


Generalize experience to novel
situations