Jump to Content
Sherry Moore

Sherry Moore

Authored Publications
Google Publications
Other Publications
Sort By
  • Title
  • Title, desc
  • Year
  • Year, desc
    Robotic Table Tennis: A Case Study into a High Speed Learning System
    Jon Abelian
    Saminda Abeyruwan
    Michael Ahn
    Justin Boyd
    Erwin Johan Coumans
    Omar Escareno
    Wenbo Gao
    Navdeep Jaitly
    Juhana Kangaspunta
    Satoshi Kataoka
    Gus Kouretas
    Yuheng Kuang
    Corey Lynch
    Thinh Nguyen
    Ken Oslund
    Barney J. Reed
    Anish Shankar
    Avi Singh
    Grace Vesom
    Peng Xu
    Robotics: Science and Systems (2023)
    Preview abstract We present a deep-dive into a learning robotic system that, in previous work, was shown to be capable of hundreds of table tennis rallies with a human and has the ability to precisely return the ball to desired targets. This system puts together a highly optimized and novel perception subsystem, a high-speed low-latency robot controller, a simulation paradigm that can prevent damage in the real world and also train policies for zero-shot transfer, and automated real world environment resets that enable autonomous training and evaluation on physical robots. We complement a complete system description including numerous design decisions that are typically not widely disseminated, with a collection of ablation studies that clarify the importance of mitigating various sources of latency, accounting for training and deployment distribution shifts, robustness of the perception system, and sensitivity to policy hyper-parameters and choice of action space. A video demonstrating the components of our system and details of experimental results is included in the supplementary material. View details
    Preview abstract Neural networks have proven effective at solving difficult problems but designing their architectures can be challenging, even for image classification problems alone. Evolutionary algorithms provide a technique to discover such networks automatically. Despite significant computational requirements, we show that evolving models that rival large, hand-designed architectures is possible today. We employ simple evolutionary techniques at unprecedented scales to discover models for the CIFAR-10 and CIFAR-100 datasets, starting from trivial initial conditions. To do this, we use novel and intuitive mutation operators that navigate large search spaces. We stress that no human participation is required once evolution starts and that the output is a fully-trained model. Throughout this work, we place special emphasis on the repeatability of results, the variability in the outcomes and the computational requirements. View details
    TensorFlow: A system for large-scale machine learning
    Jianmin Chen
    Manjunath Kudlur
    Rajat Monga
    Benoit Steiner
    Paul Tucker
    Vijay Vasudevan
    Pete Warden
    Yuan Yu
    Xiaoqiang Zheng
    12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16), USENIX Association (2016), pp. 265-283
    Preview abstract TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom-designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexibility to the application developer: whereas in previous “parameter server” designs the management of shared state is built into the system, TensorFlow enables developers to experiment with novel optimizations and training algorithms. TensorFlow supports a variety of applications, with a focus on training and inference on deep neural networks. Several Google services use TensorFlow in production, we have released it as an open-source project, and it has become widely used for machine learning research. In this paper, we describe the TensorFlow dataflow model and demonstrate the compelling performance that Tensor- Flow achieves for several real-world applications. View details
    TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
    Ashish Agarwal
    Ian Goodfellow
    Andrew Harp
    Yangqing Jia
    Rafal Jozefowicz
    Lukasz Kaiser
    Manjunath Kudlur
    Dan Mané
    Rajat Monga
    Chris Olah
    Mike Schuster
    Jonathon Shlens
    Benoit Steiner
    Ilya Sutskever
    Kunal Talwar
    Paul Tucker
    Vijay Vasudevan
    Pete Warden
    Yuan Yu
    Xiaoqiang Zheng
    tensorflow.org (2015)
    Preview abstract TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms. A computation expressed using TensorFlow can be executed with little or no change on a wide variety of heterogeneous systems, ranging from mobile devices such as phones and tablets up to large-scale distributed systems of hundreds of machines and thousands of computational devices such as GPU cards. The system is flexible and can be used to express a wide variety of algorithms, including training and inference algorithms for deep neural network models, and it has been used for conducting research and for deploying machine learning systems into production across more than a dozen areas of computer science and other fields, including speech recognition, computer vision, robotics, information retrieval, natural language processing, geographic information extraction, and computational drug discovery. This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google. The TensorFlow API and a reference implementation were released as an open-source package under the Apache 2.0 license in November, 2015 and are available at www.tensorflow.org. View details
    No Results Found