The Birth of Doraemon

TAIPEI | SEP. 21-22, 2016

Hao Wu, Cheng Hsin Lee 2016/09/21

THE BIRTH OF DORAEMON

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AGENDA

Whats a Robot

The birth of DoraemonFunctions and Application scenarios

Difficulties and ChallengesAI2 for Service Robot

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ABOUT US

Tide Technology (Beijing) Ltd.

2014.4 2015.7 2016.9

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WHATS A ROBOT

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WHATS A ROBOT

Perception Thinking Decision-making Implementation

Robot is made of a complex system

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WHY DO DORAEMON

All of us have a dream in childhood,to have one Doraemon.

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THE BIRTH OF DORAEMON

R&D for over 1 year,10+ professional teams close cooperation,more than 200 engineers effort,6 times optimizing on prototypes!

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THE FUNCTION OF DORAEMON

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APPLICATION PERSPECTIVE

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PRESENTATIONListen to reading in familiar voice

Solutionto acquire users Voice Model through deep learning method

PracticeReading for 23 minutes36 sentences

Application scenariosReading news for elder in Childrens voice

Reading story for child in Mothers voice

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PRESENTATIONEmotion Recognition

To analyze emotion via face recognition.

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DIFFICULTIES AND CHALLENGES

Tegra TX1

AI2 FOR SERVICE ROBOTAI2 = AI Application & Integration

Resources1. Algorithm2. Data3. Calculation capability

Machine Learning1.Unsupervised2.Supervised3.Reinforcement learning

AI2 + Robot1.EnvironmentPerceive2.PeopleUnderstand&Communication3.Autonomous Learning

1.Visual Perception & Understanding

(Supervised Deep Learning)

2.Strategic Dialogue System

(Deep Reinforcement Learning)

PROJECTS

1. Training Env.

2. Run time Env.

HARDWARE

OS Ubuntu 14.04

CUDA Nvidia

Performance Libarary - Nvidia

for Visual Perception : Framework Torch (facebook open source), Neural Talk

For Strategic Dialogue System : Framework ConvNet, SimpleDS

Dataset : Microsoft COCO & ImageNet

SOFTWARE

Global Opened Dataset Global Opened Model : VGGNet

PROJECT : DATASET & MODEL

CNN

object recognition

RNN

language model

PROJECT : ARCHITECTURE

Smart home environment

PROJECT : TRANSFER OF LEARNING

PROJECT : FINDING

PROJECT : APPROACHDeep Reinforcement Learning

PROJECT

Strategic DialogueDeep Reinforcement

LearningStrategic Dialog System

For compliance

REFERENCE

CVPR 2015 Paper Deep Visual-Semantic Alignments for Generating Image Descriptions Andrej Karpathy, Li Fei-Fei http://www.cs.toronto.edu/~frossard/post/vgg16/ H. Cuayhuitl. SimpleDS: A Simple Deep Reinforcement Learning Dialogue System. International

Workshop on Spoken Dialogue Systems (IWSDS), 2016 https://www.cs.utexas.edu/~eladlieb/RLRG.html

TAIPEI | SEP. 21-22, 2016

Thank you