Deep Learning for Robotics
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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. Proprietary and confidential. Do not distribute. Deep Learning for Robotics Yinyin Liu, PhD MAKING MACHINES SMARTER.™ now part of
- 2. Nervana Systems Proprietary 2 neon deep learning framework train deployexplore nervana engine 2-3x speedup on NVIDIA GPUs cloudn
- 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. 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
- 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. Nervana Systems Proprietary 11 ~60 million parameters Positive/ negative End-to-end learning Raw image input Output
- 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. Nervana Systems Proprietary 13 (Zeiler and Fergus, 2013)
- 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. 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. 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. Nervana Systems Proprietary 17 Image classification Object localization Image segmentation
- 18. Nervana Systems Proprietary 18 pepper jibo Robot base FURo-i Cubic Budgee Branto echoroomba Consumer robots for companionship and home service
- 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. Nervana Systems Proprietary 20 DL-based NLP/NLU solutions help robot to understand verbal commands and interact with users
- 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. 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. 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. 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. 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. 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. 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. Nervana Systems Proprietary 28 End-to-end learningRaw perception Output https://storage.googleapis.com/deepmind-data/assets/papers/DeepMindNature14236Paper.pdf
- 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. 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. 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
- 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. Nervana Systems Proprietary 38 neon Theano Caffe Torch TensorFlow Academic Research Bleeding-edge Curated models Iteration Time Inference speed Package ecosystem Support
- 42. Nervana Systems Proprietary 42 • github.com/NervanaSystems/ModelZoo • model files, parameters
- 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/
Editor's Notes
- A little background of who we are, what we do.
- Like to see a show of hands: who has heard of some or most of these terminologies who uses DL for their day-to-day work?
- In this talk, we will cover several topics as follows. Start with …. After each of the topics, I will be happy to pause the take any questions you have.
- In the last four years, deep learning has made its way into the heart of almost every AI application. And across all these domains, deep learning architectures have been very successful, blowing away the competition. On the surface, it seems almost unreasonably effective across these domains. First image: Tumor positive, negative
- End-to-end object localization system. Model on the left is able to learn to map each of the pixel into a category. Model on the right is able to generate bounding boxes for the precise location of the detected objects.
- Video recognition using 3D convolution.
- End to end Speech Recognition using Deep Learning Why is this cool? - ASR systems around for a long time - End to end means starting with raw spectrogram data, a deep neural net can produce text - without any hand-engineering of features - network learns features from data
- Combine Deep network with RL, a network is able to learn to play games from scratch. Similar technology being developed further by DeepMind became the algorithm behind AlphaGo.
- Things like HOG and SIFT features. Nearest neighbor clustering, L2 distance. Able to replace the nonlinear classifier by a linear classifier, because the multi layer model is nonlinear.
- Historical perspective Input → designed features → output Input → designed features → SVM → output Input → learned features → SVM → output Input → levels of learned features → output One key features of DL is that the model tends to be a end-to-end system, in the sense that, for example, if we want to classify an image, we pass the NxN raw pixels directly to a deep neural network as input, and we provide the desired output labels, and if all goes well, the model will automatically find the right features from the data. * conceptual shift in thinking, from: "how do you engineer the best features?" to "how do you guide the model towards finding the best features? but many old practices from machine learning still apply! The end-to-end nature of deep learning allows it a wide range of applicability. One could substitue videos instead of images, for example – or speech, or text, .. The output could be object identity, hair style, or a set of optimal moves in a video games.
- With DL: Features are learned from data rather than hand-engineered - at multiple levels of representation - L1 lines/bands - L3 intricate patterns (honeycomb) - L5 faces 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
- Here are just some of the examples where the Nervana platform has been applied to real-world problems such as Detecting tumors in healthcare, counting plants in agricultural robotics, finding oil rich regions in seismic data, building better speech interfaces in cars, building a timeseries search engine for finance, and engineering better organisms through amino acid sequence analysis
- In this talk, we will cover several topics as follows. Start with …. After each of the topics, I will be happy to pause the take any questions you have.
- The DL progress on computer vision obviously help robotic vision as well. For models that can do image segmentation and object localization, they definitely help a robot or an agent with scene understand and navigate around an environment.
- Inevitably, the natural language processing is an instrumental piece as well. A module as part of the robot should able to understand human command and execute on the command
- In this talk, we will cover several topics as follows. Start with …. After each of the topics, I will be happy to pause the take any questions you have.
- As one network approximating the Q value, and output layer represents values for each action, the algorithm deals with discrete and finite-set of actions only.
- Code makes it look real
- In this talk, we will cover several topics as follows. Start with …. After each of the topics, I will be happy to pause the take any questions you have.
- So where do the various frameworks match on to these attributes?
- Training is the pain point, and we are 2-3x faster than any other framework out there. That is today; when are new chip comes out we are going to be 10-20x faster than the nearest competitor. Models that originally would take you 1 month to train will take <2 days to train. This is a gamechanger. Training speed = performance.. The more iterations you can fit the better tuned your model will be for performance. In order to leverage this speed, you need resources for both training and inference. There is a reason the industry is moving to a cloud solution – it is much more cost and labor-efficient then trying to build an internal on-premise compute cluster, especially one that can scale with your computing and inference demands and return results with low latency.For example, image you wanted to deploy 100 trained models – you can buy 100 GPU boxes for each model to do inference, or you can use a DL cloud solution like us to flexibly handle the resources and scale with demand. We also provide enterprise-level customer support, which is actually very important. You are paying us; if you run into issues getting your model to train or have a bug fix that is needed for your model, we are here to provide fast support and guidance. Or if you need a speed-up and there is a GPU kernel we can write that can increase your speed by 50%, we are here. Good luck submitting an issue to an open-source framework such as caffe or waiting for Google to get back to you. This will save customers a lot of time and headache in the long run, especially folks that are relatively new to deep learning.
- don’t have to make a model from scratch - many examples of pre-trained models mention Yinyin-Fast-RCNN, Sathish C3D, babI