Kaijen Hsiao at AI Frontiers: Adorable Intelligence

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Join us to hear how we created Kuri, the world’s most adorable home robot, and how cuteness and machine learning algorithms come together in creating a robot that people are excited to bring into their homes. Kuri uses embedded, deep-learning-based algorithms for face, pet, and person detection, as well as for place recognition (for mapping and localization). Such algorithms are crucial for enabling her endearing interactions with people, as well as her ability to be the family videographer and entertainer. Cute behaviors also enable Kuri to subtly and smoothly gather necessary data for training and inference, providing a significant benefit in improving core functionality for adorable home robots.

Données & analyses

pet & person detection
place recognition
moment selection
face detection

Kuri’s embedded GPU: if we want to process 5 frames a second, we can
only use ~2.5 GMACs/frame
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Howard, Andrew G. et al. arXiv preprint 2017 (all authors from Google)
Framework Model mAP Billion
Mult-Adds
Single Shot
Multibox Detector
300x300
VGG 21.1% 34.9
Inception V2 22.0% 3.8
MobileNet 19.3% 1.2
pet & person detection

•Uses feature vectors from intermediate layer of object detection network
•Cosine distance of vectors -> difference score
Useful for:
Mapping loop closures
Global localization
“ConvNet features for Place Recognition,” Sunderhauf et al, IROS 2015
place recognition

face detection
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Zhang, Kaipeng. et al. Signal Processing Letters, 2016

BEEP BLOOP
KAIJEN HSIAO | ROBOTICIST & CTO | @KURIROBOT

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