Deep learning approaches to grasp synthesis: A review
Gras** is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …
Unfolding the literature: A review of robotic cloth manipulation
The realm of textiles spans clothing, households, healthcare, sports, and industrial
applications. The deformable nature of these objects poses unique challenges that prior …
applications. The deformable nature of these objects poses unique challenges that prior …
Habitat 2.0: Training home assistants to rearrange their habitat
Abstract We introduce Habitat 2.0 (H2. 0), a simulation platform for training virtual robots in
interactive 3D environments and complex physics-enabled scenarios. We make …
interactive 3D environments and complex physics-enabled scenarios. We make …
Domain randomization for transferring deep neural networks from simulation to the real world
Bridging thereality gap'that separates simulated robotics from experiments on hardware
could accelerate robotic research through improved data availability. This paper explores …
could accelerate robotic research through improved data availability. This paper explores …
Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes
Y ** of household objects
Using synthetic data for training deep neural networks for robotic manipulation holds the
promise of an almost unlimited amount of pre-labeled training data, generated safely out of …
promise of an almost unlimited amount of pre-labeled training data, generated safely out of …