Deep Learning Approaches to Grasp Synthesis: A Review-Reference-Cited by-同舟云学术

Deep Learning Approaches to Grasp Synthesis: A Review

Published:2023-10 Issue:5 Volume:39 Page:3994-4015
ISSN:1552-3098
Container-title:IEEE Transactions on Robotics
language:
Short-container-title:IEEE Trans. Robot.

Author:

Newbury Rhys¹^ORCID,Gu Morris¹,Chumbley Lachlan¹,Mousavian Arsalan²^ORCID,Eppner Clemens²^ORCID,Leitner Jürgen¹^ORCID,Bohg Jeannette³^ORCID,Morales Antonio⁴^ORCID,Asfour Tamim⁵^ORCID,Kragic Danica⁶^ORCID,Fox Dieter²,Cosgun Akansel⁷^ORCID

Affiliation:

1. Monash University, Clayton, VIC, Australia

2. NVIDIA Corporation, Seattle, WA, USA

3. Stanford University, Stanford, CA, USA

4. Jaume I University, Castellón de la Plana, Spain

5. Karlsruhe Institute of Technology, Karlsruhe, Germany

6. KTH Royal Institute of Technology, Stockholm, Sweden

7. Deakin University, Burwood, VIC, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/8860/10271771/10149823.pdf?arnumber=10149823

Reference213 articles.

1. Learning Task-Oriented Grasping From Human Activity Datasets

2. Active Acoustic Contact Sensing for Soft Pneumatic Actuators

3. Same object, different grasps: Data and semantic knowledge for task-oriented grasping;murali;Proc Conf Robot Learn,0

4. Swoosh! Rattle! Thump!-Actions that sound;gandhi;Robotics,2020

5. Collision-Aware Target-Driven Object Grasping in Constrained Environments

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