victores2014thesis-presentation

Robot Imagination System
Author: Juan Carlos González Víctores
Advisors: Carlos Balaguer and Alberto Jardón
Doctorado en Ingeniería Eléctrica, Electrónica y Automática – Julio 2014

 Introduction
 State of the Art
 General Architecture
 Perception
 Inference
 Execution
 Experiments
 Conclusions
1 of 45Doctorado en Ingeniería Eléctrica, Electrónica y Automática – Julio 2014

Introduction
State of the Art
Global Architecture
Perception, Inference and Execution
Experiments
Conclusions
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Origin of this Thesis
Main Objectives
Target Scenario
Expected Novelties

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Main Objectives
Target Scenario
Expected Novelties

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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A. No perceptual links between physical
characteristics and words.
Main Objectives
Target Scenario
Expected Novelties

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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B. Lack of inference capabilities for
interpreting combinations of words.
Main Objectives
Target Scenario
Expected Novelties

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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C. No bindings between actions and their
effects, reprogramming required.
Main Objectives
Target Scenario
Expected Novelties

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Main Objectives
Target Scenario
Expected Novelties
A. Develop a framework for linking
physical characteristics and words.
B. Enable inference mechanisms for
C. Allow a robot to act according to the
desired effects on an object.

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Main Objectives
Target Scenario
Expected Novelties

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Main Objectives
Target Scenario
Expected Novelties
1. Imagination
2. Grounded Language
3. Spatial Language
4. Execution
5. Integration

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Main Objectives
Target Scenario
Expected Novelties
1. Imagination
We define Robot Imagination as the process of
creating previously unexisting Knowledge K’ from
existing Knowledge K and Words W.
Where new Knowledge K’ can be used for a real
world application.
- Robot Imagination goes beyond Prediction.

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Artificial Cognitive Systems
The Symbol Grounding Problem
Mental Models and Mental Imagery
Robot Task Execution

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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``The horizontal
purple rectangle
below the horizontal
green rectangle'‘
(DESCRIBER, 2002)

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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(AARON, 1989)

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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(AARON, 1989) (AHA! Experience, 2011)
!

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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C. No bindings between actions and their
effects, reprogramming required.

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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General Overview
Component Breakdown
State Machine

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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General Overview
Component Breakdown
State Machine
A. Perception
Imagination Core
State Machine
Execution Core
Visual OutputComputer Vision
Grounding Core
Speech OutputSpeech Recognition
B. Inference C. Execution

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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General Overview
Component Breakdown
State Machine
A. Perception

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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• Computer Vision
General Overview
Component Breakdown
State Machine
A. Perception
Computer Vision

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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• Computer Vision
General Overview
Component Breakdown
State Machine
• Speech Recognition
A. Perception
Computer Vision
Speech Recognition

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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• Computer Vision
General Overview
Component Breakdown
State Machine
• Semantic Memory
A. Perception
Computer Vision
Speech Recognition

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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• Computer Vision
General Overview
Component Breakdown
State Machine
A. Perception
Computer Vision
Speech Recognition
Grounding Core
• Semantic Memory

A. Perception
Computer Vision
Speech Recognition
State Machine
Grounding Core
Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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General Overview
Component Breakdown
State Machine
• Computer Vision
• Grounded Language
• Semantic Memory

A. Perception
Computer Vision
Speech Recognition
State Machine
Grounding Core
Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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• Semantic Memory
• Embodied Cognition
• Computer Vision
• Grounded Language
General Overview
Component Breakdown
State Machine

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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• Philosophy
• Statistical Learning
• Psychology
• Neuro-science (medical)
• Neuro-science (computer)
General Overview
Component Breakdown
State Machine
A. Perception
Computer Vision
Speech Recognition
Grounding Core
State Machine
Imagination Core
B. Inference

A. Perception
Computer Vision
Speech Recognition
Grounding Core
State Machine
Imagination Core
Execution Core
Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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• Correspondence Problem
• Robot Imitation
• Goal-Directed Actions
• Continuous Goal-Directed
Actions (CGDA)
General Overview
Component Breakdown
State Machine

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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General Overview
Component Breakdown
State Machine
A. Perception
Imagination Core
State Machine
Execution Core
Grounding Core

Say: "I am ready.
Please tell me."
Store as %2.
Switch %2.
Say: "Oh, sorry.
Could you
please repeat?"
Say: "What?
Was it correct,
yes or no?"
Say: "Okay, perfect.
I've grounded this
information on %1."
Store as %1.
Say: "I understood %1.
Is that correct?".
Switch %1.
Execute touch action
with parameters %1.
Execute draw action
with parameters %1.
Say: "Sorry, I do not
know what that is. Let's
start over again."
Imagine a %1, find
most similar object, and
return its coordinates.
Imagine a %1, and
return coordinates for
drawing a sketch.
Store extracted
features as %1 in
semantic memory.
"No"
Other
Listen
Listen
Listen Listen
"Yes"
Other
"Touch a ..."
"Draw a ..."
"This is ..."
Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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General Overview
Component Breakdown
State Machine

Say: "I am ready.
Please tell me."
Store as %2.
Switch %2.
Say: "Oh, sorry.
Could you
please repeat?"
Say: "What?
Was it correct,
yes or no?"
Say: "Okay, perfect.
I've grounded this
information on %1."
Store as %1.
Say: "I understood %1.
Is that correct?".
Switch %1.
Execute touch action
with parameters %1.
Execute draw action
with parameters %1.
Say: "Sorry, I do not
know what that is. Let's
start over again."
Imagine a %1, find
most similar object, and
return its coordinates.
Imagine a %1, and
return coordinates for
drawing a sketch.
Store extracted
features as %1 in
semantic memory.
"No"
Other
Listen
Listen
Listen Listen
"Yes"
Other
"Touch a ..."
"Draw a ..."
"This is ..."
Introduction
State of the Art
Global Architecture
Experiments
Conclusions
General Overview
Component Breakdown
State Machine
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Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Perception Block
Inference Block
Execution Block
A. Perception
Computer Vision

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Perception Block
Computer Vision
Grounding Core
Inference Block
Execution Block
• 2D
• RGB/HSV or Canny Edge Detection
• Morphological Closing
• Find contours (blobs) to segment objects
• 3D
• Remove NaN and Voxelize
• RANSAC using plane model
• Remove planes (tables and walls)
• Euclidian Clustering to segment objects
• Feature extraction
• Centroid, area, rotation, min/max axis,
aspect ratio, rectangularity, solidity, arc,
radius, RGB, HSV.
A. Perception
Computer Vision

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Grounding Core
A. Perception
Perception Block
Computer Vision
Grounding Core
Inference Block
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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• Generated semantic subspaces:
Grounding Core
A. Perception
Perception Block
Computer Vision
Grounding Core
Inference Block
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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• Blue
0
Solidity
Hue
Grounding Core
A. Perception
Perception Block
Computer Vision
Grounding Core
Inference Block
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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• Blue
• Scissors
0
Solidity
Hue
0
Solidity
Hue
Grounding Core
A. Perception
Perception Block
Computer Vision
Grounding Core
Inference Block
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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0
Solidity
Hue
0
Solidity
Hue
• Blue
• Scissors
Grounding Core
A. Perception
Perception Block
Computer Vision
Grounding Core
Inference Block
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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0
Solidity
Hue
0
Solidity
Hue
• Blue
• Scissors
1
Grounding Core
A. Perception
Perception Block
Computer Vision
Grounding Core
Inference Block
Execution Block

1
Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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0
Solidity
Hue
0
Solidity
Hue
Hue
• Blue
• Scissors
• Red
Solidity
0
Grounding Core
A. Perception
Perception Block
Computer Vision
Grounding Core
Inference Block
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
Perception Block
Inference Block
Execution Block

• Object Feature Prediction:
• Basic Prediction Algorithm (RIS)
• Enhanced Prediction Algorithm (RIS)
• Object Recognition (Euclidean Distance)
• Reconstruction (Evolutionary Computation)
Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
• Basic Prediction:
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
Perception Block
Inference Block
Imagination Core
Execution Block

1. Generalization
2. Intersection
3. Projection
4. Averaging
Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
• COMPLETE
• RELEVANT
• BALANCED
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
Why a hyperplane of order n-1 ?
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
• From the algebraic definition, if the points fit
to the model, it simultaneously captures all of
the linear dependencies and couplings in Rn.
Perception Block
Inference Block
Imagination Core
Execution Block

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State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
• Hyperplanes are defined in all space, so the
“meanings” of words are also extended across,
and along, all the Feature Space.
Perception Block
Inference Block
Imagination Core
Execution Block

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State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
• Hyperplanes are defined in all space, so the
“meanings” of words are also extended across,
and along, all the Feature Space.
• Practical benefit of always finding
intersections between these representations.
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
Why are orthogonal projections used ?
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
• Projections result on the valid solution
geometrical construct, and are closest to the
original data of a given point cloud.
Perception Block
Inference Block
Imagination Core
Execution Block

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State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
• Projections result on the valid solution
geometrical construct, and are closest to the
original data of a given point cloud.
• Promotes ranges of values that are common in
objects, which is useful when not all features are
specified by words.
Perception Block
Inference Block
Imagination Core
Execution Block

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State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
Does the algorithm have any known limitations?
Perception Block
Inference Block
Imagination Core
Execution Block

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State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
Yes, the basic prediction algorithm may fail under
the following circumstances:
Perception Block
Inference Block
Imagination Core
Execution Block

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State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
• Context dependency (e.g. “green” color or state
of ripeness).
Perception Block
Inference Block
Imagination Core
Execution Block

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State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
of ripeness).
• Periodicity (e.g. “red” hue is 0º and 360º, a
hyperplane could be fit at 180º).
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Generalization
2. Intersection
3. Projection
4. Averaging
• COMPLETE
• RELEVANT
• BALANCED
of ripeness).
• Periodicity (e.g. “red” hue is 0º and 360º, a
hyperplane could be fit at 180º).
• Specificity (non-extendable meanings).
Perception Block
Inference Block
Imagination Core
Execution Block

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
• Enhanced Prediction:
1. Context Detector
2. Hyperspherical
Shape Detector
Perception Block
Inference Block
Imagination Core
Execution Block

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State of the Art
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Experiments
Conclusions
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Imagination Core
B. Inference
1. Context Detector
2. Hyperspherical
Shape Detector
The Context Detector counts the accompanying
words of a query word within each cluster
Perception Block
Inference Block
Imagination Core
Execution Block

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State of the Art
Global Architecture
Experiments
Conclusions
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Imagination Core
B. Inference
1. Context Detection
2. Hyperspherical
Shape Detector The Hyperspherical Shape Detector compares all
the sorted eigenvalues of a given point cloud
Perception Block
Inference Block
Imagination Core
Execution Block

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State of the Art
Global Architecture
Experiments
Conclusions
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Execution Core
C. Execution
Perception Block
Inference Block
Execution Block

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State of the Art
Global Architecture
Experiments
Conclusions
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Execution Core
C. Execution
Perception Block
Inference Block
Execution Block
Execution Core
• Action Generalization (CGDA)
• Action Recognition (CGDA)
• Execution (Evolutionary Computation)

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State of the Art
Global Architecture
Experiments
Conclusions
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Execution Core
C. Execution
Perception Block
Inference Block
Execution Block
Execution Core

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State of the Art
Global Architecture
Experiments
Conclusions
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Execution Core
C. Execution
Perception Block
Inference Block
Execution Block
Execution Core
• Action Generalization
1. Split in Intervals

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Global Architecture
Experiments
Conclusions
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Execution Core
C. Execution
Perception Block
Inference Block
Execution Block
Execution Core
2. Average in Intervals

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State of the Art
Global Architecture
Experiments
Conclusions
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Execution Core
C. Execution
Perception Block
Inference Block
Execution Block
Execution Core
2. Average in Intervals
3. Linear Interpolation

GeneralizedTraj.e.g.Xpos
Query Traj. e.g. X pos
Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Execution Core
C. Execution
Perception Block
Inference Block
Execution Block
Execution Core

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Global Architecture
Experiments
Conclusions
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Execution Core
C. Execution
Perception Block
Inference Block
Execution Block
Execution Core
• Action Recognition
1. Axis-by-axis comparison

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State of the Art
Global Architecture
Experiments
Conclusions
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Execution Core
C. Execution
Perception Block
Inference Block
Execution Block
Execution Core
2. Dynamic Time Warping

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State of the Art
Global Architecture
Experiments
Conclusions
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Execution Core
C. Execution
Perception Block
Inference Block
Execution Block
Execution Core
2. Dynamic Time Warping
3. Total cost = sum(DTW)

A. Perception
Imagination Core
State Machine
Execution Core
Grounding Core
Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Basic Prediction Algorithm: Drawing
Enhanced Prediction: Spatial Language
Execution Core: Waxing
The Token Test
Each RIS component has been
implemented as a YARP module.
They are direct implementations of
the explained algorithms.

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State of the Art
Global Architecture
Experiments
Conclusions
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The Token Test
1. Populating the database: Every training sample has a description

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Global Architecture
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Conclusions
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The Token Test
1. Populating the database: Every training sample has a description,
e.g. the first image is labeled “top-left-dark-blue-fat-straight-box”

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Conclusions
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The Token Test
3. The mental models are created through Object Reconstruction (using
evolutionary computation) aiming at the predicted features, e.g. the first
image responds to the query “bottom right”, the second to “bottom left”,
the third to “top right”, and the last to “top blue”.
2. Queries are summited upon the populated database,
e.g. “bottom right” or “bottom left”.

3. The mental models are created through Object Reconstruction (using
evolutionary computation) aiming at the predicted features, e.g. the first
image responds to the query “bottom right”, the second to “bottom left”,
the third to “top right”, and the last to “top blue”.
Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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The Token Test
2. Queries are summited upon the populated database,
e.g. “bottom right” or “bottom left”

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Conclusions
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The Token Test
4.The drawing demonstration, where the robot exposes its mental model.
[J.G.Victores, S.Morante et Al., IROS 2013,Tokyo] [FASTCompany, 2013]

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State of the Art
Global Architecture
Experiments
Conclusions
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The Token Test
-The system is trained through speech with a blue visual marker.
-The component used for Speech Recognition requires a pre-defined
corpus of words to recognize.

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State of the Art
Global Architecture
Experiments
Conclusions
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The Token Test
The system can generalize a word with as little as 2 training
samples in the 2D case, and 3 training samples in the 3D case.

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State of the Art
Global Architecture
Experiments
Conclusions
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The Token Test
Testing the execution of the “waxing” action encoded as a
Continuous Goal-Oriented Action (CGDA).

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State of the Art
Global Architecture
Experiments
Conclusions
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The Token Test
[S.Morante, J.G.Victores et Al., ICRA 2013, Hong Kong]

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Global Architecture
Experiments
Conclusions
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The Token Test

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State of the Art
Global Architecture
Experiments
Conclusions
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The Token Test

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State of the Art
Global Architecture
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Perception
- Framework for creating links between words and
physical characteristics
Inference
- Robot Imagination System for providing robots with
imagination skills through object features and semantics
Execution
- Presented a new way of encoding actions, which can
complement Programming by Demonstration
- Explained proposed algorithms for CGDA
generalization, recognition and execution
Progress beyond the State of Art
Future Lines of Research
Scientific Contributions

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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Origin of Data
- Sensory information of various nature (e.g. haptic,
rugosity) and from the Internet-of-Things.
Data Representation
- Human-inspired color space such as YUV. Use of SIFT and
SURF visual descriptors.
Techniques for Inference
- Feature selection (e.g. LASSO). Gaussian Mixture Models,
Non-linear PCA, Manifolds, Neural Networks.
Additional Functionalities
- Vector and dynamic information, auto-tuned parameters.

Introduction
State of the Art
Global Architecture
Experiments
Conclusions
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“Action Effect Generalization, Recognition and Execution
through Continuous Goal-Directed Actions”, IEEE ICRA,
Hong Kong, China, 2014.
“Towards Robot Imagination Through Object Feature
Inference”, IEEE/RSJ IROS,Tokyo, Japan, 2013.
“Personal Autonomy Rehabilitation in Home
Environments by a Portable Assistive Robot”, IEEE-
Systems, Man and Cybernetics, Part C, 2011.
Over 27 conference articles, 4 top-quartile journal papers,
3 book chapters, a patent, and artistic works have been
published by the author within the duration of this Thesis.

victores2014thesis-presentation

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