Review of deep reinforcement learning-based object gras**: Techniques, open challenges, and recommendations
MQ Mohammed, KL Chung, CS Chyi - IEEE Access, 2020 - ieeexplore.ieee.org
The motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …
reinforcement learning-based object manipulation. Various studies are examined through a …
Physgen: Rigid-body physics-grounded image-to-video generation
We present PhysGen, a novel image-to-video generation method that converts a single
image and an input condition (eg., force and torque applied to an object in the image) to …
image and an input condition (eg., force and torque applied to an object in the image) to …
NeuralSim: Augmenting differentiable simulators with neural networks
Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the
use of efficient, gradient-based optimization algorithms to find the simulation parameters that …
use of efficient, gradient-based optimization algorithms to find the simulation parameters that …
Clear grasp: 3d shape estimation of transparent objects for manipulation
S Sajjan, M Moore, M Pan, G Nagaraja… - … on robotics and …, 2020 - ieeexplore.ieee.org
Transparent objects are a common part of everyday life, yet they possess unique visual
properties that make them incredibly difficult for standard 3D sensors to produce accurate …
properties that make them incredibly difficult for standard 3D sensors to produce accurate …
Gras** in the wild: Learning 6dof closed-loop gras** from low-cost demonstrations
Intelligent manipulation benefits from the capacity to flexibly control an end-effector with high
degrees of freedom (DoF) and dynamically react to the environment. However, due to the …
degrees of freedom (DoF) and dynamically react to the environment. However, due to the …