Deep learning approaches to grasp synthesis: A review
Grasping 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 …
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review
This paper presents a comprehensive survey on vision-based robotic grasping. We
conclude three key tasks during vision-based robotic grasping, which are object localization …
conclude three key tasks during vision-based robotic grasping, which are object localization …
Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes
M Sundermeyer, A Mousavian… - … on Robotics and …, 2021 - ieeexplore.ieee.org
Grasping unseen objects in unconstrained, cluttered environments is an essential skill for
autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning …
autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning …
Dexpoint: Generalizable point cloud reinforcement learning for sim-to-real dexterous manipulation
We propose a sim-to-real framework for dexterous manipulation which can generalize to
new objects of the same category in the real world. The key of our framework is to train the …
new objects of the same category in the real world. The key of our framework is to train the …
Rgb matters: Learning 7-dof grasp poses on monocular rgbd images
General object grasping is an important yet unsolved problem in the field of robotics. Most of
the current methods either generate grasp poses with few DoF that fail to cover most of the …
the current methods either generate grasp poses with few DoF that fail to cover most of the …
Graspness discovery in clutters for fast and accurate grasp detection
Efficient and robust grasp pose detection is vital for robotic manipulation. For general 6 DoF
grasping, conventional methods treat all points in a scene equally and usually adopt uniform …
grasping, conventional methods treat all points in a scene equally and usually adopt uniform …
Transcg: A large-scale real-world dataset for transparent object depth completion and a grasping baseline
Transparent objects are common in our daily life and frequently handled in the automated
production line. Robust vision-based robotic grasping and manipulation for these objects …
production line. Robust vision-based robotic grasping and manipulation for these objects …
Data-driven robotic visual grasping detection for unknown objects: A problem-oriented review
This paper presents a comprehensive survey of data-driven robotic visual grasping
detection (DRVGD) for unknown objects. We review both object-oriented and scene …
detection (DRVGD) for unknown objects. We review both object-oriented and scene …
Review of deep reinforcement learning-based object grasping: 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 …
Neural grasp distance fields for robot manipulation
We formulate grasp learning as a neural field and present Neural Grasp Distance Fields
(NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a …
(NGDF). Here, the input is a 6D pose of a robot end effector and output is a distance to a …