337 citations · 1.2k across the 7 of their papers we have counts for
8 papers
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…
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…
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…
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…
Domain Randomization and Generative Models for Robotic Grasping
Joshua Tobin, Lukas Biewald, Rocky Duan +8
Deep learning-based robotic grasping has made significant progress thanks to algorithmic improvements and increased data availability. However, state-of-the-art models are often tr…
Hindsight Experience Replay
Marcin Andrychowicz, Filip Wolski, Alex Ray +7
Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL). We present a novel technique called Hindsight Experience Replay which allows sample-eff…
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…
Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model
Paul Christiano, Zain Shah, Igor Mordatch +5
Developing control policies in simulation is often more practical and safer than directly running experiments in the real world. This applies to policies obtained from planning and…