Deep Learning in robotics - Tecnalia · PDF fileDeep Learning in robotics JORNADA DEEP...
Transcript of Deep Learning in robotics - Tecnalia · PDF fileDeep Learning in robotics JORNADA DEEP...
Deep Learning
in robotics
JORNADA DEEP LEARNING: LA REVOLUCIÓN TECNOLÓGICA DE LA INTELIGENCIA ARTIFICIAL
Jon Azpiazu [email protected]
ROBOTICS IN TECNALIA
INDUSTRY AND TRANSPORT Division HEALTH Division
ROBOT as a PRODUCT ROBOT as a Tool to automate process
ROBOT Autonomy as a key to Flexibility
Deep Learning in Robotics
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Deep Learning in Robotics
Deep Learning in Robotics
ImageNet Error Rate 2010-2014
Source: ClarifAI
Deep Learning in Robotics
Source: ClarifAI
Deep Learning in Robotics
ImageNet Error Rate 2010-2014
Pixels Hand-engineered Interest Points (SIFT, SURF, Fast, …)
Matching Distance Classifier (SVM)
Label “cat” “screw” …
Audio Acoustic Model Phonetic Model Language Model
Hand-engineered Descriptors (SIFT, SURF, HOG, …)
Text
Deep Learning in Robotics
Speech Recognition Computer Vision
Pixels
Label “cat” “screw” …
Audio Deep Neural Network Text
Deep Learning in Robotics
Speech Recognition Computer Vision
Deep Neural Network
Pixels
Label “cat” “screw” …
Audio Deep Neural Network Text
Deep Learning in Robotics
Speech Recognition Computer Vision Robotics
Deep Neural Network
Sensors Perception State estimation
Planning Control Motor commands
Pixels
Label “cat” “screw” …
Audio Deep Neural Network Text
Deep Learning in Robotics
Speech Recognition Computer Vision Robotics
Deep Neural Network
Sensors Motor commands
Deep Neural Network
Deep Learning in Robotics
Goal
Reward Actions
Observations
Environment Agent
Additional challenges: • Stability • Credit assignment • Exploration
Deep Learning in Robotics
Deep Learning in Robotics
• End-to-end learning of values Q(s,a) from pixels s • Input state s is stack of raw pixels from last 4 frames • Output is Q(s,a) for 18 joystick/button positions • Reward is change in score for that step
Deep Learning in Robotics
Objective: full Autopilot by 2018 • 780 million miles in 18 months
(2014) • 1 million miles every 10 hours • Google: 1.5 million miles (2009)
• Simulation: 3 million miles a day
Deep Learning in Robotics
• Robot Bin Picking with Deep Learning
• Learning Contact-Rich Manipulation Skills with Guided Policy Search
• Learning Hand-Eye Coordination for
Robotic Grasping with Deep Learning
Deep Learning in Robotics
Open challenges: • Data • Transfer learning / Shared learning • Memory • Decision making (goals)
References
Pieter Abbeel (UC Berkeley) - DL for Robotics ["DeepLearning in Robotics", RSS 2016] https://www.youtube.com/watch?v=mT7HMTTCI1k Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013). Raia Hadsell, “Deep Learning for Robots”, European Robotics Forum 2017 (ERF2017) Levine, Sergey, et al. "Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection." arXiv preprint arXiv:1603.02199 (2016). Sergey Levine, “Deep Robotic Learning”, 4th International Conference on Learning Representations (ICLR 2017)
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