Hao Wu Cheng Hsin Lee

1. TAIPEI | SEP. 21-22, 2016
Hao Wu, Cheng Hsin Lee 2016/09/21
THE BIRTH OF DORAEMON

2. 2
AGENDA
What’s a Robot
The birth of Doraemon
Functions and Application scenarios
Difficulties and Challenges
AI2 for Service Robot

3. 3
ABOUT US
潮汐科技（北京）有限公司
Tide Technology (Beijing) Ltd.
2014.4 2015.7 2016.9

4. 4
WHAT’S A ROBOT

5. 5
WHAT’S A ROBOT
Perception Thinking Decision-making Implementation
Robot is made of a complex system

6. 6
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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PRESENTATION
Listen 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

12. 12
PRESENTATION
Emotion Recognition
To analyze emotion via face recognition.

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DIFFICULTIES AND CHALLENGES
Tegra TX1

14. AI2 FOR SERVICE ROBOT
AI2 = AI Application & Integration
Resources
1. Algorithm
2. Data
3. Calculation capability
Machine Learning
1.Unsupervised
2.Supervised
3.Reinforcement learning
AI2 + Robot
1.Environment：Perceive
2.People：Understand&Communicatio
3.Autonomous Learning

15. 1.Visual Perception & Understanding
(Supervised Deep Learning)
2.Strategic Dialogue System
(Deep Reinforcement Learning)
PROJECTS

16. 1. Training Env.
2. Run time Env.
HARDWARE

17. 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

18. Global Opened Dataset Global Opened Model : VGGNet
PROJECT : DATASET & MODEL

19. CNN
object recognition
RNN
language model
PROJECT : ARCHITECTURE

20. Smart home environment
PROJECT : TRANSFER OF LEARNING

21. PROJECT : FINDING

22. PROJECT : APPROACH
Deep Reinforcement Learning

23. PROJECT
Strategic Dialogue
Deep Reinforcement
Learning
Strategic Dialog System
For compliance

24. 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

25. TAIPEI | SEP. 21-22, 2016
Thank you