CSG6120-01인지과학특강 I인지과학특강 I

(Theory of Mind for Robots)

Spring Semester, 2011

Teaching Staff Lecturer

- Sung-Bae Cho (C515; 2123-2720; sbcho@cs.yonsei.ac.kr)- Kyung-Joong Kim ( 3408-3838, kimkj@sejong.ac.kr)y g g ( , j@ j g )

Hours : 화요일오후 2~4시, 오후 5시~6시

Place : A542

Office Hours : 화요일 4시~5시

Robot and Cognitive ScienceCognitive RoboticsRobot Psychology

Experimental Robotics Developmental Robotics

Bio-Inspired Robotics

CognitiveScience

Robotics

Cross Fertilization

• Biological Inspiration• Computational Model

CognitiveScience

Robotics

• Engineering Inspiration• Engineering Inspiration• Virtual Humans and Animals

• Reverse Engineering Cognitive Functions

Theory of Mind

Theory of mind is the ability to attribute mental states—beliefs, intents,

desires pretending knowledge etc to oneself and others and todesires, pretending, knowledge, etc.—to oneself and others and to

understand that others have beliefs, desires and intentions that are

different from one's own

from wikipedia

Course Goals

Theory of Mind에대한이해및최신연구동향파악

인지과학의연구도구로Robotics 기술이해

Robot을위한 Theory of Mind 관련연구이해및연구주제수행 y

Course Materials

Reading in Theory of Mind for Robots– Theoretical Foundations for Theory of Mind

• Theory, Neuroscience, Animal Study ….

– Applications for Simulated Robotspp• Virtual Character, Robot Simulator

A li ti f R l R b t– Applications for Real-Robots • Real Robots (E-Pucks, Humanoids, …)

Week 2시간 1시간

논문발표 (학생)

Week 2시간 1시간1 Course Introduction

2E-Puck 로봇소개

E Puck Simulator소개 신경망 진화연산소개2 E-Puck Simulator 소개IR 센서소개

신경망, 진화연산소개

3 PhysX 소개OptiTrack 소개 진화신경망소개OptiTrack 소개

4 ToM 기초논문들 Simulated Robot을위한 ToM

5 ToM을로봇에적용한대표사례논문들

Real Robot을위한 ToM논문들

6 EEA 알고리즘및그응용 제공 SW 소개7 Term Project Proposal8 Midterm Exam8 Midterm Exam9 로봇을위한 ToM 프로젝트진행상황 Discussion

10 석가탄신일 석가탄신일11 로봇을위한 ToM 프로젝트진행상황 Discussion11 로봇을위한 ToM 프로젝트진행상황 Discussion12 로봇을위한 ToM 프로젝트진행상황 Discussion13 로봇을위한 ToM 프로젝트진행상황 Discussion14 로봇을위한 ToM 프로젝트진행상황 Discussion14 로봇을위한 ToM 프로젝트진행상황 Discussion15 Term Project Final Presentation16 Final Exam

Resources

E-Pucks Optical Tracking Devices (OptiTrack) IR Light Source (SiMa Night Vision)

Evaluation Criteria

Evaluation Criteria– Term Project (written report and an oral presentation) : 70%– Class Presentation : 20%– Class Participation : 10%

Term Project (Oral presentation is required) :– Theoretical Issue (Analysis, Experiment, Simulation) : Originality

I t ti P i (G D t ) P f– Interesting Programming (Game, Demo, etc) : Performance– Survey : Completeness

Basics in Theory of MindBasics in Theory of Mind

Mind Reading

Non-Human Theory of MindNon Human Theory of MindThey Can Do?

Theory of Mind Cartoons

Non Theory of Mind Cartoons

Recursive Nature of ToM

Robotics Approach(Self Modeling)

Robotics Approach(Theory of Mind)

Theory of Mind for Humanoids [MIT, 2000]

GazingFace DetectionFace Detection Infer Simple Intention

[Brian Scassellati][ ]

Mario et al. (2007)

Robotics Approach(Neural-basis)

Theory of Mind in Robotic Society

A: I Know “You”! B: I Know “You Know Me”!A I K “Y K I K Y ”!A: I Know “You Know I Know You”!B: …

Predator Pray

Scenario

1 Infer Other’s Brain 2 Exploitation

From Virtual To Real

1 PhysX 2 EnKi 3 E-Puck

Controller (Neural Network)

b1b4w7

Steering AngleLeft Light Sensor Value w1w2

b2

bw7

w8w3

w4

b2

b5

w9

w10VelocityRight Light Sensor Value

w4w5

w6b3

b5w11w10

w6w12

-0.509588

-0.385503

-0.104083

0 172043

0.238755

0.172043

0.272120

0 1922550.192255

-0.973429

9.644485

66.393120

[Original] [Reconstructed]speed[Original] [ eco s uc ed]

Sens

or V

alue

Sens

or V

alue

Lef

t Lig

ht S

Lef

t Lig

ht S

Ri ht Li ht S V l Right Light Sensor ValueRight Light Sensor Value Right Light Sensor Value

Real Robot Scenarios

Possible ProjectsPossible Projects

Overview

Robot

B h i

ToM

Behavior (Trajectories)

인공신경망(Robot’s Mind)

알고리즘

EstimatedOne

Possible Project Lists I (Survey)

Theory of Mind를설명하는이론들에대한조사

Brain-Imaging을통해밝혀진 Theory of Mind 관련사실들조사

인간이아닌다른동물들의 Theory of Mind에대한조사 y

Theory of Mind를테스트하는방법에대한조사

봇시 레이션Possible Project II (로봇시뮬레이션) ToM알고리즘의종료조건테스트 (다양한조건들중에어떤것이최적인 ToM알고리즘의종료조건테스트 (다양한조건들중에어떤것이최적인지실험적으로테스트)

T M알고리즘에서상대방로봇의행동데이터들을어떻게활용하는것이 ToM알고리즘에서상대방로봇의행동데이터들을어떻게활용하는것이최적인지테스트 (가장최근에얻어낸데이터만이용/과거데이터도모두이용/가중치를부여하여이용)

ToM알고리즘에서상대방로봇의행동데이터를어떻게얻는것이최적인지테스트 (random하게얻어온다/능동적으로필요한데이터를얻어온다)다)

ToM알고리즘에서두 Trajectory가닮았다는것을어떻게측정하는것이j y좋은가? (Euclidean Distance, Derivatives …)

ToM알고리즘에서상대방로봇의행동데이터를사용할때그 trajectory ToM알고리즘에서상대방로봇의행동데이터를사용할때그 trajectory의각 segment가서로다른중요도를가진다고보고, 그중요도를최적화한다.

실제 봇Possible Project III (실제로봇)

Reality Gap (Simulator와실제로봇) 최소화– OptiTrack이용– Sensor Models (서로다른정밀도지님) – Motor Adjustment Parameters – Reality Gap이얼마나줄어드는지평가y p

Immature Neural Controller 진화단순히직진으로빛을따라가는최적화한제어기보다는– 단순히직진으로빛을따라가는최적화한제어기보다는

– 회전하거나커브를그리는형태의제어기를선호– Long-Term Exposure Shot으로로봇행동촬영하기