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Proprietary and confidential. Do not distribute.
Deep Learning for Robotics
Yinyin Liu, PhD
MAKING MACHINES
SMARTER.™
now ...
Nervana Systems Proprietary
2
neon deep
learning
framework
train deployexplore
nervana
engine
2-3x speedup on
NVIDIA GPUs
...
Nervana Systems Proprietary
3
Back-propagation
End-to-end
Resnet
ImageNet
NLP
Regularization
Convolution
Unrolling
RNN
Gen...
Nervana Systems Proprietary
4
• What is Deep Learning and What Can It Do Today?
• How DL helps Robotics?
• Deep Reinforcem...
Nervana Systems Proprietary
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Nervana Systems Proprietary
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https://www.nervanasys.com/industry-focus-serving-the-automotive-industry-with-the-nervana-p...
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http://www.nervanasys.com/deep-reinforcement-learning-with-neon/
https://youtu.be/KkIf0Ok5GCE
Nervana Systems Proprietary
10
Historical perspective:
• Input → designed features → output
• Input → designed features → ...
Nervana Systems Proprietary
11
~60 million parameters
Positive/
negative
End-to-end learning
Raw image input Output
Nervana Systems Proprietary
12
A method for extracting features at multiple
levels of abstraction
• Features are discovere...
Nervana Systems Proprietary
13
(Zeiler and Fergus, 2013)
Nervana Systems Proprietary
Source: ImageNet
ImageNet top 5 error rate
0%
10%
20%
30%
2010 2011 2012 2013 2014 2015
human
...
Nervana Systems Proprietary
15
Healthcare: Tumor detection
Automotive: Speech interfaces Finance: Time-series search engin...
Nervana Systems Proprietary
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• What is Deep Learning and What Can It Do Today?
• How DL helps Robotics?
• Deep Reinforce...
Nervana Systems Proprietary
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Image classification Object localization
Image segmentation
Nervana Systems Proprietary
18
pepper jibo
Robot base FURo-i Cubic Budgee Branto
echoroomba
Consumer robots for companions...
Nervana Systems Proprietary
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https://www.autonomous.ai/personal-robot
DL-based computer vision solutions help robot to n...
Nervana Systems Proprietary
20
DL-based NLP/NLU solutions help robot to understand verbal commands and interact with users
Nervana Systems Proprietary
21
• But most of the consumer robots either do not move or move around on a base
• Home robots...
Nervana Systems Proprietary
22
• Trying to tackle the problem of robotic grasping
• 14 Separate robots to collect data in ...
Nervana Systems Proprietary
23
Prediction network: CNN learn to predict the outcome of a grasp, given
• An image before gr...
Nervana Systems Proprietary
24
Servoing mechanism:
• User the predictor network
• Choose the motor commands from a pool of...
Nervana Systems Proprietary
25
• End-to-end learning
what are objects vs. gripper
what is the right orientation to grasp
w...
Nervana Systems Proprietary
26
• What is Deep Learning and What Can It Do Today?
• How DL helps Robotics?
• Deep Reinforce...
Nervana Systems Proprietary
27
• RL – defines the goal, reward, training paradigm
• DL – gives the mechanics
• RL + DL = A...
Nervana Systems Proprietary
28
End-to-end learningRaw perception Output
https://storage.googleapis.com/deepmind-data/asset...
Nervana Systems Proprietary
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https://www.nervanasys.com/demystif
ying-deep-reinforcement-learning/
https://www.nervanasy...
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https://github.com/tambetm/simple_dqn
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https://github.com/tambetm/simple_dqn
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• As one network approximating the Q value, and output layer represents values for each act...
Nervana Systems Proprietary
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• What is Deep Learning and What Can It Do Today?
• How DL helps Robotics?
• Deep Reinforce...
Nervana Systems Proprietary
34
To make progress on robotics:
• Need a lot of data to improve on executing tasks
• Need int...
Nervana Systems Proprietary
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https://arxiv.org/pdf/1604.06778v3.pdf
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https://www.nervanasys.com/openai/
Nervana Systems Proprietary
Layers
Linear, Convolution, Pooling, Deconvolution, Dropout, Recurrent, Long Short-
Term Memor...
Nervana Systems Proprietary
38
neon Theano Caffe Torch TensorFlow
Academic Research
Bleeding-edge
Curated models
Iteration...
Nervana Systems Proprietary
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Third-party
(Facebook)
benchmarking
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• github.com/NervanaSystems/ModelZoo
• model files, parameters
Nervana Systems Proprietary
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neon
https://github.com/NervanaSystems/neon
Nervana’s deep learning tutorials:
https://www....
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Deep Learning for Robotics

Yinyin Liu presents at SD Robotics Meetup on November 8th, 2016. Deep learning has made great success in image understanding, speech, text recognition and natural language processing. Deep Learning also has tremendous potential to tackle the challenges in robotic vision, and sensorimotor learning in a robotic learning environment. In this talk, we will talk about how current and future deep learning technologies can be applied for robotic applications.

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Deep Learning for Robotics

  1. 1. Proprietary and confidential. Do not distribute. Deep Learning for Robotics Yinyin Liu, PhD MAKING MACHINES SMARTER.™ now part of
  2. 2. Nervana Systems Proprietary 2 neon deep learning framework train deployexplore nervana engine 2-3x speedup on NVIDIA GPUs cloudn
  3. 3. Nervana Systems Proprietary 3 Back-propagation End-to-end Resnet ImageNet NLP Regularization Convolution Unrolling RNN Generalization hyperparameters Video recognition dropout Pooling LSTM AlexNet Auto-encoder neon https://github.com/NervanaSystems/neon Nervana’s deep learning tutorials: https://www.nervanasys.com/deep-learning-tutorials/ We are hiring! https://www.nervanasys.com/careers/
  4. 4. Nervana Systems Proprietary 4 • What is Deep Learning and What Can It Do Today? • How DL helps Robotics? • Deep Reinforcement Learning • Finding the Right Frameworks For You
  5. 5. Nervana Systems Proprietary 5
  6. 6. Nervana Systems Proprietary 6 https://www.nervanasys.com/industry-focus-serving-the-automotive-industry-with-the-nervana-platform/
  7. 7. Nervana Systems Proprietary 7
  8. 8. Nervana Systems Proprietary 8
  9. 9. Nervana Systems Proprietary 9 http://www.nervanasys.com/deep-reinforcement-learning-with-neon/ https://youtu.be/KkIf0Ok5GCE
  10. 10. Nervana Systems Proprietary 10 Historical perspective: • Input → designed features → output • Input → designed features → SVM → output • Input → learned features → SVM → output • Input → levels of learned features → output
  11. 11. Nervana Systems Proprietary 11 ~60 million parameters Positive/ negative End-to-end learning Raw image input Output
  12. 12. Nervana Systems Proprietary 12 A method for extracting features at multiple levels of abstraction • Features are discovered from data • Performance improves with more data • Network can express complex transformations • High degree of representational power
  13. 13. Nervana Systems Proprietary 13 (Zeiler and Fergus, 2013)
  14. 14. Nervana Systems Proprietary Source: ImageNet ImageNet top 5 error rate 0% 10% 20% 30% 2010 2011 2012 2013 2014 2015 human performance • No free lunch • lots of data • flexible and fast frameworks • powerful computing resources 14
  15. 15. Nervana Systems Proprietary 15 Healthcare: Tumor detection Automotive: Speech interfaces Finance: Time-series search engine Positive: Negative: Agricultural Robotics Oil & Gas Positive: Negative: Proteomics: Sequence analysis Query: Results:
  16. 16. Nervana Systems Proprietary 16 • What is Deep Learning and What Can It Do Today? • How DL helps Robotics? • Deep Reinforcement Learning • Finding the Right Frameworks For You
  17. 17. Nervana Systems Proprietary 17 Image classification Object localization Image segmentation
  18. 18. Nervana Systems Proprietary 18 pepper jibo Robot base FURo-i Cubic Budgee Branto echoroomba Consumer robots for companionship and home service
  19. 19. Nervana Systems Proprietary 19 https://www.autonomous.ai/personal-robot DL-based computer vision solutions help robot to navigate around a home and understand the scene and localize everyday objects.
  20. 20. Nervana Systems Proprietary 20 DL-based NLP/NLU solutions help robot to understand verbal commands and interact with users
  21. 21. Nervana Systems Proprietary 21 • But most of the consumer robots either do not move or move around on a base • Home robots are still far from providing home service, e.g. cooking, cleaning, taking care of people. • Robot movement is a difficult • It is challenging for robot to know how to interact with objects, not to mention having the level of dexterity of human
  22. 22. Nervana Systems Proprietary 22 • Trying to tackle the problem of robotic grasping • 14 Separate robots to collect data in parallel, 800k grasp attempts collected, over 7 months • Each grasp consists of T time steps. At the end of the T, grasp success is evaluated. Then T samples of (image, current pose, success label) data are collected • No human labelling needed! Levine et.al (2016) https://research.googleblog.com/2016/03/deep- learning-for-robots-learning-from.html
  23. 23. Nervana Systems Proprietary 23 Prediction network: CNN learn to predict the outcome of a grasp, given • An image before grasp begins • An image at current time • A motor command - 3D translation vector https://arxiv.org/pdf/1603.02199v4.pdf
  24. 24. Nervana Systems Proprietary 24 Servoing mechanism: • User the predictor network • Choose the motor commands from a pool of samples with the best score Prediction network score score score score
  25. 25. Nervana Systems Proprietary 25 • End-to-end learning what are objects vs. gripper what is the right orientation to grasp what is the right motor command • Learn from repetitively trials • A useful training paradigm is RL
  26. 26. Nervana Systems Proprietary 26 • What is Deep Learning and What Can It Do Today? • How DL helps Robotics? • Deep Reinforcement Learning • Finding the Right Frameworks For You
  27. 27. Nervana Systems Proprietary 27 • RL – defines the goal, reward, training paradigm • DL – gives the mechanics • RL + DL = AI* http://icml.cc/2016/tutorials/deep_rl_tutorial.pdf * By David Silver
  28. 28. Nervana Systems Proprietary 28 End-to-end learningRaw perception Output https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf
  29. 29. Nervana Systems Proprietary 29 https://www.nervanasys.com/demystif ying-deep-reinforcement-learning/ https://www.nervanasys.com/deep- reinforcement-learning-with-neon/
  30. 30. Nervana Systems Proprietary 30 https://github.com/tambetm/simple_dqn
  31. 31. Nervana Systems Proprietary 31 https://github.com/tambetm/simple_dqn
  32. 32. Nervana Systems Proprietary 32 • As one network approximating the Q value, and output layer represents values for each action, the algorithm deals with discrete and small finite-set of actions only. • Apply actor-critic architecture to continuous action space • Add BatchNorm – help to generalize to different problems • High-dimensional tasks simulated in MuJoCo. • Race game simulated using Torcs. Lillicrap et. al. (Deepmind, ICLR 2016) https://arxiv.org/pdf/1509.02971v5.pdf
  33. 33. Nervana Systems Proprietary 33 • What is Deep Learning and What Can It Do Today? • How DL helps Robotics? • Deep Reinforcement Learning • Finding the Right Frameworks For You
  34. 34. Nervana Systems Proprietary 34 To make progress on robotics: • Need a lot of data to improve on executing tasks • Need interaction with the environment - costly for real world experiments - need simulator for a variety of tasks • Need benchmarks - ImageNet drove a lot of progress for the vision problems in supervised learning - lack of standardized environment, tasks, or metrics for RL publications and comparison
  35. 35. Nervana Systems Proprietary 35 https://arxiv.org/pdf/1604.06778v3.pdf
  36. 36. Nervana Systems Proprietary 36 https://www.nervanasys.com/openai/
  37. 37. Nervana Systems Proprietary Layers Linear, Convolution, Pooling, Deconvolution, Dropout, Recurrent, Long Short- Term Memory, Gated Recurrent Unit, BatchNorm, LookupTable, Local Response Normalization, Bidirectional-RNN, Bidirectional-LSTM Backend NervanaGPU, NervanaCPU, NervanaMGPU Datasets MNIST, CIFAR-10, Imagenet 1K, PASCAL VOC, Mini-Places2, IMDB, Penn Treebank, Shakespeare Text, bAbI, Hutter-prize, UCF101, flickr8k, flickr30k, COCO Initializers Constant, Uniform, Gaussian, Glorot Uniform, Xavier, Kaiming, IdentityInit, Orthonormal Optimizers Gradient Descent with Momentum, RMSProp, AdaDelta, Adam, Adagrad,MultiOptimizer Activations Rectified Linear, Softmax, Tanh, Logistic, Identity, ExpLin Costs Binary Cross Entropy, Multiclass Cross Entropy, Sum of Squares Error Metrics Misclassification (Top1, TopK), LogLoss, Accuracy, PrecisionRecall, ObjectDetection 37
  38. 38. Nervana Systems Proprietary 38 neon Theano Caffe Torch TensorFlow Academic Research Bleeding-edge Curated models Iteration Time Inference speed Package ecosystem Support
  39. 39. Nervana Systems Proprietary 39 Third-party (Facebook) benchmarking
  40. 40. Nervana Systems Proprietary 40
  41. 41. Nervana Systems Proprietary 41
  42. 42. Nervana Systems Proprietary 42 • github.com/NervanaSystems/ModelZoo • model files, parameters
  43. 43. Nervana Systems Proprietary 43 neon https://github.com/NervanaSystems/neon Nervana’s deep learning tutorials: https://www.nervanasys.com/deep-learning-tutorials/ We are hiring! https://www.nervanasys.com/careers/

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