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 …
A survey on learning-based robotic grasping
Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic grasping and manipulation. Current trends and …
learning approaches for vision-based robotic grasping and manipulation. Current trends and …
Bounding box regression with uncertainty for accurate object detection
Large-scale object detection datasets (eg, MS-COCO) try to define the ground truth
bounding boxes as clear as possible. However, we observe that ambiguities are still …
bounding boxes as clear as possible. However, we observe that ambiguities are still …
Recent advances on loss functions in deep learning for computer vision
The loss function, also known as cost function, is used for training a neural network or other
machine learning models. Over the past decade, researchers have designed many loss …
machine learning models. Over the past decade, researchers have designed many loss …
A review of robot learning for manipulation: Challenges, representations, and algorithms
A key challenge in intelligent robotics is creating robots that are capable of directly
interacting with the world around them to achieve their goals. The last decade has seen …
interacting with the world around them to achieve their goals. The last decade has seen …
Grasping in the wild: Learning 6dof closed-loop grasping 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 …
Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation
We present a coarse-to-fine discretisation method that enables the use of discrete
reinforcement learning approaches in place of unstable and data-inefficient actor-critic …
reinforcement learning approaches in place of unstable and data-inefficient actor-critic …
S4g: Amodal single-view single-shot se (3) grasp detection in cluttered scenes
Grasping is among the most fundamental and long-lasting problems in robotics study. This
paper studies the problem of 6-DoF (degree of freedom) grasping by a parallel gripper in a …
paper studies the problem of 6-DoF (degree of freedom) grasping by a parallel gripper in a …
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 …
Object rearrangement using learned implicit collision functions
M Danielczuk, A Mousavian… - 2021 IEEE International …, 2021 - ieeexplore.ieee.org
Robotic object rearrangement combines the skills of picking and placing objects. When
object models are unavailable, typical collision-checking models may be unable to predict …
object models are unavailable, typical collision-checking models may be unable to predict …