The Birth of Doraemon

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  • TAIPEI | SEP. 21-22, 2016

    Hao Wu, Cheng Hsin Lee 2016/09/21

    THE BIRTH OF DORAEMON

  • 2

    AGENDA

    Whats a Robot

    The birth of DoraemonFunctions and Application scenarios

    Difficulties and ChallengesAI2 for Service Robot

  • 3

    ABOUT US

    Tide Technology (Beijing) Ltd.

    2014.4 2015.7 2016.9

  • 4

    WHATS A ROBOT

  • 5

    WHATS A ROBOT

    Perception Thinking Decision-making Implementation

    Robot is made of a complex system

  • 6

    WHY DO DORAEMON

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

  • 7

    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!

  • 9

    THE FUNCTION OF DORAEMON

  • 10

    APPLICATION PERSPECTIVE

  • 11

    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

  • 12

    PRESENTATIONEmotion Recognition

    To analyze emotion via face recognition.

  • 13

    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