ReviewFrontiers in neurorobotics2021
Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning.
Review in Frontiers in neurorobotics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 56 citations in OpenAlex.
- A Hybrid 2D-3D RGB-D Method for Growth Axis Estimation and Robotic Grasp Pose Generation of Phalaenopsis Orchid Buds.MethodsX · 2026Article
- Learning Playing Piano with Bionic-Constrained Diffusion Policy for Anthropomorphic Hand.Cyborg and bionic systems (Washington, D.C.) · 2024Article
- Emerging trends and research foci of deep learning in spine: bibliometric and visualization study.Neurosurgical review · 2023Review
- 1D Barcode Detection: Novel Benchmark Datasets and Comprehensive Comparison of Deep Convolutional Neural Network Approaches.Sensors (Basel, Switzerland) · 2022Article
- Dexterous Manipulation for Multi-Fingered Robotic Hands With Reinforcement Learning: A Review.Frontiers in neurorobotics · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dexterous manipulation, especially dexterous grasping, is a primitive and crucial ability of robots that allows the implementation of performing human-like behaviors. Deploying the ability on robots enables them to assist and substitute human to accomplish more complex tasks in daily life and industrial production. A comprehensive review of the methods based on point cloud and deep learning for robotics dexterous grasping from three perspectives is given in this paper. As a new category schemes of the mainstream methods, the proposed generation-evaluation framework is the core concept of the classification. The other two classifications based on learning modes and applications are also briefly described afterwards. This review aims to afford a guideline for robotics dexterous grasping researchers and developers.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.