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Towards Cognitive Robotics
Christian Goerick
Honda Research Institute Europe GmbH
Patrick Emaase
Biointelligence Laboratory
School of Computer Science and Engineering
Seoul National University
http://bi.snu.ac.kr
Contents
1 Introduction
2 Towards an Architecture
3 Task and Body Oriented Motion Control
4 Visually and Behaviorally Oriented Learning
5
ALIS
– Autonomous Learning and Interactive
6 Conclusion
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
2
The Big Picture
Architecture
Vision, Behavior
ALIS
Cognitive
Robot
Schematics / Repre

How to realize Cognitve Robot
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
3
Introduction

Long term goals


Create humanoid robot equipped with mechanisms for
learning and development – dynamically, robustly
Understand and re-create how human brain works

Research vehicle: Humanoid robot

PISA – Practical Intelligent Systems Architecture;

Architecture: Strategic Organization and
incremental systems

Major issue: Learning and adaptation – interaction with real world
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
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Towards an Architecture: PISA

Cognitive Robot
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
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© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
Intelligent Behavior
Learn and reason
Achieves complex
goals
Acts, perceives,
plans, anticipates
5
Motion
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
6
Task & Body Oriented Motion Control

Identify task accurately, move easily - complex

Have level of intelligence as humans & animals
control effectors for tasks easy

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Have Body image – helps acting in complex task
Desirable cognitive architecture: able to cognitively
control relevant task parameters, leave “tedious”
details to underlying levels of control
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
7
ASIMO Robot: Kinematics


Stable layer for motion control with motion interface has
been established – solve collision
Robot controlled by task level description, the coupling is
performed by whole body controller

Implements redundant control scheme considers all DoF at once
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
8
Visually and Behaviorally Oriented
Learning

Goal: Provide Humanoid with Interactive behavior,
vision, adaptability

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Autonomous development mechanisms
Interactive Learning mechanisms

Emphasis: Principled combination of both (A, IL)

Biologically motivated Interactive vision System
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Adaptive basic behavior – can learn and recognize
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
9
Active Vision System

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Active: Recognizes images, re-plans view points
Determine new direction based on saliency + previous gaze direction
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
10
Concept of space

Two types of space:
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Peripersonal space establishes “Sharing Attention”

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Peripersonal space and Extrapersonal space
User show object, system focus on shown entity
Addressed scientific concepts


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Online learning
Internal homeostatic control system
Combination of both
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
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ALIS: Autonomous Learning and
Interacting System

Has incremental hierarchical system comprising
sensing and control elements

System interacts in real time with users

Architecture: hierarchical mimicking biological brain
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
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SYSTEMATICA

Framework SYSTEMATICA – For describing
incremental hierarchical control architecture
n is identifiable unit
 X – full input space
 D – dynamics
 R – representations
 B – behavior space
 T – top-down info
 P – priority
 S – sensory space
 M – motor commands
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
13
SYSTEMATICA
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Sensory Sn(X) and Behavior space Bn(X) split into
location and features aspects.
Framework characterizes architecture,
decomposes units n consisting of Sn(x), Dn, Bn, Rn,
Mn, Pn, Tm,n to allow system:
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Incremental learning
Always act
Provide representations and decompositions
Necessary conditions to achieve SYSTEMATICA is
hierarchical arrangement of sensory and behavior
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
14
Biological Embedding of SYSTEMATICA

To achieve brain-like intelligence


Synergistic interplay of diff. level of hierarchy
Dynamic architecture

Brain modeled as inhibition of sensory signals and
motor commands

Deeper communication between units plausible
and beneficial

Efficient in (re)-using est. representations & processes
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
15
ALIS: Architecture and Elements

ALIS represents
incrementally
integrated system

Elements are
hierarchically
arranged

Produce observable
behavior
Schematics of ALIS formulated from SYSTEMATICA
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
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So far….
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Goal: Create Brain Like Intelligence
Motivation: Human brain,
Concepts: Active Learning, adaptability, Autonomy
Architecture: PISA, Systematica
Achievement: Advanced Step in Innovative Mobility
(ASIMO) {Humanoid}
Challenges: Stability, incremental knowledge
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
17
Conclusion
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ALIS System has independent units built
Incremental hierarchy yields combined
performance enabling
Combines autonomy and ability to learn, develop


Towards Cognitive Robotics
Researching and creating in an incremental and
holistic fashion leads to better understanding of
natural and artificial brain-like systems
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
18
Review Question

What do we gain by pursuing task description and
whole body control in cognitive architecture?

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Description of tasks in natural way than in joint space
 High level process don’t care about details of motion
Motion range is extended incrementally
Understand DoF redundancy in movement &
correspondence to hand actions & adaption to force
Solve acceptance problems with robots
Self collision avoidance on the level of motion control
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
19
Review Question

What do we gain by pursuing such kind of task
description and whole body control in cognitive
architecture?





Description of tasks in natural way than in joint space.
 High level process don’t care about details of motion
Motion range is extended incrementally – appearance of
robot motion is naturally relaxed
Understand DoF redundancy in movement and
correspondence to hand actions & adaption to force
Solve acceptance problems with robots
Self collision avoidance on the level of motion control
© 2015, SNU CSE Biointelligence Lab., http://bi.snu.ac.kr
20
Thank you for listening
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