Deep learning approaches to grasp synthesis: A review

R Newbury, M Gu, L Chumbley… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
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 …

A survey on learning-based robotic grasping

K Kleeberger, R Bormann, W Kraus, MF Huber - Current Robotics Reports, 2020 - Springer
Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic grasping and manipulation. Current trends and …

Bounding box regression with uncertainty for accurate object detection

Y He, C Zhu, J Wang, M Savvides… - Proceedings of the …, 2019 - openaccess.thecvf.com
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 …

Recent advances on loss functions in deep learning for computer vision

Y Tian, D Su, S Lauria, X Liu - Neurocomputing, 2022 - Elsevier
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 …

A review of robot learning for manipulation: Challenges, representations, and algorithms

O Kroemer, S Niekum, G Konidaris - Journal of machine learning research, 2021 - jmlr.org
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 …

Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations

S Song, A Zeng, J Lee… - IEEE Robotics and …, 2020 - ieeexplore.ieee.org
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 …

Coarse-to-fine q-attention: Efficient learning for visual robotic manipulation via discretisation

S James, K Wada, T Laidlow… - Proceedings of the …, 2022 - openaccess.thecvf.com
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 …

S4g: Amodal single-view single-shot se (3) grasp detection in cluttered scenes

Y Qin, R Chen, H Zhu, M Song… - Conference on robot …, 2020 - proceedings.mlr.press
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 …

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 …

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 …