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Smarter Humanoid
Companion
Embedded GPUs can make your robotic companion
more alive
Alexandre Mazel Innovation Software Director
Oct 2017
Hi Alex!
Agenda
● Overview○ Innovation Team Presentation○ Mummer Research Project
● Problematics
● Proposed solution
● Live Demo
● Question
Team Presentation
Part of the Innovation Department, which includes hardware,
electronics, collaborative projects and design.
AI Lab
Fundamental Research on Developmental Robotics
3 Permanent3 PhD student
3 Intern
Protolab
Applied Research
4 Permanent1 PhD student
Innovation Software
Goal: Prospection
Enhance our Humanoid Robots for more natural Human-Robot Interaction
● Explore future uses ● Test and embed new algorithms● Hardware improvement● Provide versatile platforms for research
Goals:● Make Pepper navigates in malls● Entertain visitors/customers
Experimentation field: ● Ideapark mall in Lempäälä (Finland)● Huge: More than 150 stores, restaurants and cafes
within 100.000 m2● Crowdy: 7 million visitors (2013)
SoftBank Robotics Europe
VTT TechnicalResearch Center of Finland
Ideapark
University of Glasgow
Heriot-Watt University
Idiap Research Institute
LAAS-CNRS
Challenges
● Obstacle avoidance
● Quick person detection (<1s)
● Self Localization
● Data confidentiality
Navigation Sensors
Laser (45 points, up to 3m)
RGB Camera (55°H, 44°V)
Sonar
Depth Camera (58°H, 45°V)RGB Camera (55°H, 44°V)
ConvNet Learning for obstacle avoidance
● pretrained AlexNet using the LSVRC-2010 ImageNet (1.3M Images)
● learning FC7, FC8 and binary classification
Passable
Non
passable
Results of the learning process
● 3400 images
● Learning rate: 0.001
● dropout rate: 0.5
● batches of size: 40
● duration of one epoch: 70 sec (using a Geforce GTX 1070)
NB: Using the trained tensorflow model as is
Action Time (s)
Acquire image 0.016
Computing difference 0.005
Undistort and rotation (numpy) 0.049
Inference 0.033
Total Time 0.103 (9.7 fps)
Embedding JetsonTM TX2
Battery Draining Measure
NB: based on one test only
Standard Pepper - no movement 11h32
Pepper with gpu processing and infering every
frame - no movement10h21
Advantages
● Dodges obstacles
● Fully autonomous (no cloud, no wifi)
● Quick training - can be done multiple times
● Can be learned directly on site
● Confidentiality is preserved
To be continued
Next steps:
● add more classes (left/right/center)
● optimisation (int8, tensorRT, …)
● autonomous & continuous learning on the fly
Future work:
● Navigation: Localisation/VSlam
● Skeleton estimation (2D) (Zhe Cao, Tomas Simon, Shih-En Wei, Yaser Sheikh)
● Face features extraction
● Speech Recognition (Caldi)
Acknowledgement
Based on work from:
● Abdelhak Loukkal (2017)
● Michael Guerzhoy and Davi Frossard (2016)
Reference:
● Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks (2015)
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