Search Results for author: Vincent Vanhoucke

Found 15 papers, 10 papers with code

Differentiable Mapping Networks: Learning Structured Map Representations for Sparse Visual Localization

no code implementations19 May 2020 Peter Karkus, Anelia Angelova, Vincent Vanhoucke, Rico Jonschkowski

We address these tasks by combining spatial structure (differentiable mapping) and end-to-end learning in a novel neural network architecture: the Differentiable Mapping Network (DMN).

Visual Localization

X-Ray: Mechanical Search for an Occluded Object by Minimizing Support of Learned Occupancy Distributions

no code implementations20 Apr 2020 Michael Danielczuk, Anelia Angelova, Vincent Vanhoucke, Ken Goldberg

For applications in e-commerce, warehouses, healthcare, and home service, robots are often required to search through heaps of objects to grasp a specific target object.

Policies Modulating Trajectory Generators

2 code implementations7 Oct 2019 Atil Iscen, Ken Caluwaerts, Jie Tan, Tingnan Zhang, Erwin Coumans, Vikas Sindhwani, Vincent Vanhoucke

We propose an architecture for learning complex controllable behaviors by having simple Policies Modulate Trajectory Generators (PMTG), a powerful combination that can provide both memory and prior knowledge to the controller.

Grasp2Vec: Learning Object Representations from Self-Supervised Grasping

1 code implementation16 Nov 2018 Eric Jang, Coline Devin, Vincent Vanhoucke, Sergey Levine

We formulate an arithmetic relationship between feature vectors from this observation, and use it to learn a representation of scenes and objects that can then be used to identify object instances, localize them in the scene, and perform goal-directed grasping tasks where the robot must retrieve commanded objects from a bin.

Representation Learning

QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

1 code implementation27 Jun 2018 Dmitry Kalashnikov, Alex Irpan, Peter Pastor, Julian Ibarz, Alexander Herzog, Eric Jang, Deirdre Quillen, Ethan Holly, Mrinal Kalakrishnan, Vincent Vanhoucke, Sergey Levine

In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach.

Classification of crystallization outcomes using deep convolutional neural networks

2 code implementations27 Mar 2018 Andrew E. Bruno, Patrick Charbonneau, Janet Newman, Edward H. Snell, David R. So, Vincent Vanhoucke, Christopher J. Watkins, Shawn Williams, Julie Wilson

The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups.

Classification General Classification

Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping

1 code implementation22 Sep 2017 Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, Sergey Levine, Vincent Vanhoucke

We extensively evaluate our approaches with a total of more than 25, 000 physical test grasps, studying a range of simulation conditions and domain adaptation methods, including a novel extension of pixel-level domain adaptation that we term the GraspGAN.

Domain Adaptation Industrial Robots +1

TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow

2 code implementations8 Sep 2017 Danijar Hafner, James Davidson, Vincent Vanhoucke

We introduce TensorFlow Agents, an efficient infrastructure paradigm for building parallel reinforcement learning algorithms in TensorFlow.

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

52 code implementations23 Feb 2016 Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi

Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network.

General Classification Image Classification

Going Deeper with Convolutions

67 code implementations CVPR 2015 Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich

We propose a deep convolutional neural network architecture codenamed "Inception", which was responsible for setting the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC 2014).

Classification General Classification +3

Deep Neural Networks for Acoustic Modeling in Speech Recognition

no code implementations Signal Processing Magazine 2012 Geoffrey Hinton, Li Deng, Dong Yu, George Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath, Brian Kingsbury

Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and Gaussian mixture models to determine how well each state of each HMM fits a frame or a short window of frames of coefficients that represents the acoustic input.

Speech Recognition

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