towards cognitive robotics biointelligence laboratory school of computer science and engineering...

Towards Cognitive Robotics

Biointelligence Laboratory

School of Computer Science and Engineering

Seoul National University

http://bi.snu.ac.kr

Christian GoerickHonda Research Institute Europe GmbH

Patrick Emaase

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

How to realize Cognitve Robot


Cognitive Robot

Cognitive Robot

ArchitectureArchitecture

Vision, BehaviorVision, Behavior

Schematics / RepreSchematics / Repre

ALISALIS

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


Towards an Architecture: PISA


Cognitive Robot Intelligent Behavior Learn and reason Achieves complex

goals

Acts, perceives, plans, anticipates

Motion


Task & Body Oriented Motion Control

Identify task accurately, move easily - complex

Have level of intelligence as humans & animals control effectors for tasks easy 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


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

Visually and Behaviorally Oriented Learning

Goal: Provide Humanoid with Interactive behavior, vision, adaptability Autonomous development mechanisms Interactive Learning mechanisms

Emphasis: Principled combination of both (A, IL)

Biologically motivated Interactive vision System Adaptive basic behavior – can learn and recognize


Active Vision System


Active: Recognizes images, re-plans view points Determine new direction based on saliency + previous gaze direction

Concept of space

Two types of space: Peripersonal space and Extrapersonal space

Peripersonal space establishes “Sharing Attention” User show object, system focus on shown entity

Addressed scientific concepts Online learning Internal homeostatic control system Combination of both


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


SYSTEMATICA

Framework SYSTEMATICA – For describing incremental hierarchical control architecture


n is identifiable unitX – full input spaceD – dynamicsR – representationsB – behavior spaceT – top-down infoP – priorityS – sensory spaceM – motor commands

SYSTEMATICA

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: Incremental learning Always act Provide representations and decompositions

Necessary conditions to achieve SYSTEMATICA is hierarchical arrangement of sensory and behavior


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


ALIS: Architecture and Elements


Schematics of ALIS formulated from SYSTEMATICA

ALIS represents incrementally integrated system

Elements are hierarchically arranged

Produce observable behavior

So far….

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


Conclusion

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


Review Question

What do we gain by pursuing 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 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


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


Thank you for listening

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towards cognitive robotics biointelligence laboratory school of computer science and engineering...

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