activity
20172020
most citedDomain Randomization for Transferring Deep Neural Networks from Simulation to the Real World

205 citations · 218 across the 3 of their papers we have counts for

collaborators

5 papers

stat.ML20201 cited

Clustering of Big Data with Mixed Features

Joshua Tobin, Mimi Zhang

Clustering large, mixed data is a central problem in data mining. Many approaches adopt the idea of k-means, and hence are sensitive to initialisation, detect only spherical cluste…

cs.CV201912 cited

Geometry-Aware Neural Rendering

Josh Tobin, OpenAI Robotics, Pieter Abbeel

Understanding the 3-dimensional structure of the world is a core challenge in computer vision and robotics. Neural rendering approaches learn an implicit 3D model by predicting wha…

cs.LG2018

Learning Dexterous In-Hand Manipulation

OpenAI, Marcin Andrychowicz, Bowen Baker +14

We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand. The tra…

cs.LG2018

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

Matthias Plappert, Marcin Andrychowicz, Alex Ray +9

The purpose of this technical report is two-fold. First of all, it introduces a suite of challenging continuous control tasks (integrated with OpenAI Gym) based on currently existi…

cs.RO2017205 cited

Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World

Josh Tobin, Rachel Fong, Alex Ray +3

Bridging the 'reality gap' that separates simulated robotics from experiments on hardware could accelerate robotic research through improved data availability. This paper explores…