TAIPEI | SEP. 21-22, 2016

Hao Wu, Cheng Hsin Lee 2016/09/21

THE BIRTH OF DORAEMON

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AGENDA

What’s a Robot

The birth of DoraemonFunctions and Application scenarios

Difficulties and ChallengesAI2 for Service Robot

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ABOUT US

潮汐科技（北京）有限公司

Tide Technology (Beijing) Ltd.

2014.4 2015.7 2016.9

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WHAT’S A ROBOT

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WHAT’S 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

Solution：to acquire user’s Voice Model through deep learning method

Practice：Reading for 2～3 minutes（36 sentences）

Application scenarios：Reading news for elder in Children’s voice

Reading story for child in Mother’s 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.Environment：Perceive2.People：Understand&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. Cuayáhuitl. 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