185 citations · 350 across the 7 of their papers we have counts for
6 papers · 1 filter
OSCAR: Data-Driven Operational Space Control for Adaptive and Robust Robot Manipulation
Josiah Wong, Viktor Makoviychuk, Anima Anandkumar +1
Learning performant robot manipulation policies can be challenging due to high-dimensional continuous actions and complex physics-based dynamics. This can be alleviated through int…
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo +8
Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. Both physics simulation and the neural network policy tr…
In-Hand Object Pose Tracking via Contact Feedback and GPU-Accelerated Robotic Simulation
Jacky Liang, Ankur Handa, Karl Van Wyk +3
Tracking the pose of an object while it is being held and manipulated by a robot hand is difficult for vision-based methods due to significant occlusions. Prior works have explored…
Non-Smooth Newton Methods for Deformable Multi-Body Dynamics
Miles Macklin, Kenny Erleben, Matthias Müller +3
We present a framework for the simulation of rigid and deformable bodies in the presence of contact and friction. Our method is based on a non-smooth Newton iteration that solves t…
GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning
Jacky Liang, Viktor Makoviychuk, Ankur Handa +3
Most Deep Reinforcement Learning (Deep RL) algorithms require a prohibitively large number of training samples for learning complex tasks. Many recent works on speeding up Deep RL…
Closing the Sim-to-Real Loop: Adapting Simulation Randomization with Real World Experience
Yevgen Chebotar, Ankur Handa, Viktor Makoviychuk +4
We consider the problem of transferring policies to the real world by training on a distribution of simulated scenarios. Rather than manually tuning the randomization of simulation…