254 citations · 403 across the 11 of their papers we have counts for
4 papers · 1 filter
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement Learning
Nikita Rudin, David Hoeller, Philipp Reist +1
In this work, we present and study a training set-up that achieves fast policy generation for real-world robotic tasks by using massive parallelism on a single workstation GPU. We…
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…
Learning a State Representation and Navigation in Cluttered and Dynamic Environments
David Hoeller, Lorenz Wellhausen, Farbod Farshidian +1
In this work, we present a learning-based pipeline to realise local navigation with a quadrupedal robot in cluttered environments with static and dynamic obstacles. Given high-leve…
Articulated Object Interaction in Unknown Scenes with Whole-Body Mobile Manipulation
Mayank Mittal, David Hoeller, Farbod Farshidian +2
A kitchen assistant needs to operate human-scale objects, such as cabinets and ovens, in unmapped environments with dynamic obstacles. Autonomous interactions in such environments…