Deep learning approaches to grasp synthesis: A review
Gras** 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 gras**
Abstract Purpose of Review This review provides a comprehensive overview of machine
learning approaches for vision-based robotic gras** and manipulation. Current trends and …
learning approaches for vision-based robotic gras** and manipulation. Current trends and …
6-dof graspnet: Variational grasp generation for object manipulation
Generating grasp poses is a crucial component for any robot object manipulation task. In this
work, we formulate the problem of grasp generation as sampling a set of grasps using a …
work, we formulate the problem of grasp generation as sampling a set of grasps using a …
Acronym: A large-scale grasp dataset based on simulation
We introduce ACRONYM, a dataset for robot grasp planning based on physics simulation.
The dataset contains 17.7 M parallel-jaw grasps, spanning 8872 objects from 262 different …
The dataset contains 17.7 M parallel-jaw grasps, spanning 8872 objects from 262 different …
Hand-object contact consistency reasoning for human grasps generation
While predicting robot grasps with parallel jaw grippers have been well studied and widely
applied in robot manipulation tasks, the study on natural human grasp generation with a …
applied in robot manipulation tasks, the study on natural human grasp generation with a …
Grasp'd: Differentiable contact-rich grasp synthesis for multi-fingered hands
The study of hand-object interaction requires generating viable grasp poses for high-
dimensional multi-finger models, often relying on analytic grasp synthesis which tends to …
dimensional multi-finger models, often relying on analytic grasp synthesis which tends to …
Learning continuous 3d reconstructions for geometrically aware gras**
M Van der Merwe, Q Lu… - … on Robotics and …, 2020 - ieeexplore.ieee.org
Deep learning has enabled remarkable improvements in grasp synthesis for previously
unseen objects from partial object views. However, existing approaches lack the ability to …
unseen objects from partial object views. However, existing approaches lack the ability to …
Robotics dexterous gras**: The methods based on point cloud and deep learning
Dexterous manipulation, especially dexterous gras**, is a primitive and crucial ability of
robots that allows the implementation of performing human-like behaviors. Deploying the …
robots that allows the implementation of performing human-like behaviors. Deploying the …
Planning multi-fingered grasps as probabilistic inference in a learned deep network
Q Lu, K Chenna, B Sundaralingam… - Robotics Research: The …, 2020 - Springer
We propose a novel approach to multi-fingered grasp planning leveraging learned deep
neural network models. We train a convolutional neural network to predict grasp success as …
neural network models. We train a convolutional neural network to predict grasp success as …
Multifingered grasp planning via inference in deep neural networks: Outperforming sampling by learning differentiable models
Q Lu, M Van der Merwe… - IEEE Robotics & …, 2020 - ieeexplore.ieee.org
We propose a novel approach to multifingered grasp planning that leverages learned deep
neural network (DNN) models. We trained a voxel-based 3D convolutional neural network …
neural network (DNN) models. We trained a voxel-based 3D convolutional neural network …