Review of deep learning methods in robotic grasp detection
For robots to attain more general-purpose utility, gras** is a necessary skill to master.
Such general-purpose robots may use their perception abilities to visually identify grasps for …
Such general-purpose robots may use their perception abilities to visually identify grasps for …
Real-world multiobject, multigrasp detection
A deep learning architecture is proposed to predict graspable locations for robotic
manipulation. It considers situations where no, one, or multiple object (s) are seen. By …
manipulation. It considers situations where no, one, or multiple object (s) are seen. By …
A review of robotic grasp detection technology
M Dong, J Zhang - Robotica, 2023 - cambridge.org
In order to complete many complex operations and attain more general-purpose utility,
robotic grasp is a necessary skill to master. As the most common essential action of robots in …
robotic grasp is a necessary skill to master. As the most common essential action of robots in …
A novel robotic gras** method for moving objects based on multi-agent deep reinforcement learning
Y Huang, D Liu, Z Liu, K Wang, Q Wang… - Robotics and Computer …, 2024 - Elsevier
To grasp the randomly moving objects in unstructured environment, a novel robotic gras**
method based on multi-agent TD3 with high-quality memory (MA-TD3H) is proposed. During …
method based on multi-agent TD3 with high-quality memory (MA-TD3H) is proposed. During …
A joint network for grasp detection conditioned on natural language commands
We consider the task of gras** a target object based on a natural language command
query. Previous work primarily focused on localizing the object given the query, which …
query. Previous work primarily focused on localizing the object given the query, which …
GKNet: Grasp keypoint network for grasp candidates detection
Contemporary grasp detection approaches employ deep learning to achieve robustness to
sensor and object model uncertainty. The two dominant approaches design either grasp …
sensor and object model uncertainty. The two dominant approaches design either grasp …
Using synthetic data and deep networks to recognize primitive shapes for object gras**
A segmentation-based architecture is proposed to decompose objects into multiple primitive
shapes from monocular depth input for robotic manipulation. The backbone deep network is …
shapes from monocular depth input for robotic manipulation. The backbone deep network is …
Object affordance detection with boundary-preserving network for robotic manipulation tasks
C Yin, Q Zhang - Neural Computing and Applications, 2022 - Springer
Object affordance detection aims to identify, locate and segment the functional regions of
objects, so that robots can understand and manipulate objects like humans. The affordance …
objects, so that robots can understand and manipulate objects like humans. The affordance …
Multi-object gras** detection with hierarchical feature fusion
Gras** in cluttered and tight scenes is a necessary skill for intelligent robotics to achieve
more general application. Such universal robotics can use their perception abilities to …
more general application. Such universal robotics can use their perception abilities to …
Convolutional multi-grasp detection using grasp path for RGBD images
L Chen, P Huang, Z Meng - Robotics and Autonomous Systems, 2019 - Elsevier
Generally, most grasp detection models follow the similar frameworks as that in object
detection, which use the convolutional neural network to regress the grasp parameters …
detection, which use the convolutional neural network to regress the grasp parameters …