Search Results for author:
Found 25 papers, 12 papers with code
Attention-based Extraction of Structured Information from Street View Imagery
We present a neural network model - based on CNNs, RNNs and a novel attention mechanism - which achieves 84. 2% accuracy on the challenging French Street Name Signs (FSNS) dataset, significantly outperforming the previous state of the art (Smith'16), which achieved 72. 46%.
Ranked #1 on
Optical Character Recognition (OCR)
on FSNS - Test
Token Turing Machines
The model's memory module ensures that a new observation will only be processed with the contents of the memory (and not the entire history), meaning that it can efficiently process long sequences with a bounded computational cost at each step.
Ranked #1 on
Action Detection
on Charades
End-to-End Interpretation of the French Street Name Signs Dataset
We introduce the French Street Name Signs (FSNS) Dataset consisting of more than a million images of street name signs cropped from Google Street View images of France.
Ranked #3 on
Optical Character Recognition (OCR)
on FSNS - Test
RT-1: Robotics Transformer for Real-World Control at Scale
By transferring knowledge from large, diverse, task-agnostic datasets, modern machine learning models can solve specific downstream tasks either zero-shot or with small task-specific datasets to a high level of performance.
Do As I Can, Not As I Say: Grounding Language in Robotic Affordances
We show how low-level skills can be combined with large language models so that the language model provides high-level knowledge about the procedures for performing complex and temporally-extended instructions, while value functions associated with these skills provide the grounding necessary to connect this knowledge to a particular physical environment.
Diversity is All You Need: Learning Skills without a Reward Function
On a variety of simulated robotic tasks, we show that this simple objective results in the unsupervised emergence of diverse skills, such as walking and jumping.
Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones
Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration.
Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
In this paper we propose a unified approach that integrates these three steps via the use of a deep convolutional neural network that operates directly on the image pixels.
Ranked #30 on
Image Classification
on SVHN
Leave no Trace: Learning to Reset for Safe and Autonomous Reinforcement Learning
In this work, we propose an autonomous method for safe and efficient reinforcement learning that simultaneously learns a forward and reset policy, with the reset policy resetting the environment for a subsequent attempt.
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
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.
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
In this paper, we study the problem of learning vision-based dynamic manipulation skills using a scalable reinforcement learning approach.
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods
In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping.
Discrete Sequential Prediction of Continuous Actions for Deep RL
Specifically, we show how Q-values and policies over continuous spaces can be modeled using a next step prediction model over discretized dimensions.
Large Scale Scene Text Verification with Guided Attention
In this work, we propose a new framework that learns this task in an end-to-end way.
End-to-End Learning of Semantic Grasping
We consider the task of semantic robotic grasping, in which a robot picks up an object of a user-specified class using only monocular images.
In-Datacenter Performance Analysis of a Tensor Processing Unit
Our workload, written in the high-level TensorFlow framework, uses production NN applications (MLPs, CNNs, and LSTMs) that represent 95% of our datacenters' NN inference demand.
Large Scale Business Discovery from Street Level Imagery
Precise business store front detection enables accurate geo-location of businesses, and further provides input for business categorization, listing generation, etc.
Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks
Using domain adaptation methods to cross this "reality gap" requires a large amount of unlabelled real-world data, whilst domain randomization alone can waste modeling power.
Off-Policy Evaluation via Off-Policy Classification
However, for high-dimensional observations, such as images, models of the environment can be difficult to fit and value-based methods can make IS hard to use or even ill-conditioned, especially when dealing with continuous action spaces.
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control
We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robot must decide on the next action while still performing the previous action.
RL-CycleGAN: Reinforcement Learning Aware Simulation-To-Real
However, this sort of translation is typically task-agnostic, in that the translated images may not preserve all features that are relevant to the task.
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned
Learning to perceive and move in the real world presents numerous challenges, some of which are easier to address than others, and some of which are often not considered in RL research that focuses only on simulated domains.
Visionary: Vision architecture discovery for robot learning
We propose a vision-based architecture search algorithm for robot manipulation learning, which discovers interactions between low dimension action inputs and high dimensional visual inputs.
MESA: Offline Meta-RL for Safe Adaptation and Fault Tolerance
Safe exploration is critical for using reinforcement learning (RL) in risk-sensitive environments.
Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions
In this work, we present a scalable reinforcement learning method for training multi-task policies from large offline datasets that can leverage both human demonstrations and autonomously collected data.
