1.5k citations · 2.3k across the 7 of their papers we have counts for
5 papers · 1 filter
Asymmetric Actor Critic for Image-Based Robot Learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder +2
Deep reinforcement learning (RL) has proven a powerful technique in many sequential decision making domains. However, Robotics poses many challenges for RL, most notably training o…
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba +1
Simulations are attractive environments for training agents as they provide an abundant source of data and alleviate certain safety concerns during the training process. But the be…
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…
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…