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Get a Grip: Multi-Finger Grasp Evaluation at Scale Enables Robust Sim-to-Real Transfer

Get a Grip: Multi-Finger Grasp Evaluation at Scale Enables Robust Sim-to-Real Transfer

Tyler Ga Wei Lum
Albert H. Li
Preston Culbertson
K. Srinivasan
Aaron D. Ames
Mac Schwager
Jeannette Bohg
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Papers citing "Get a Grip: Multi-Finger Grasp Evaluation at Scale Enables Robust Sim-to-Real Transfer"

4 / 4 papers shown
Title
DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation
DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation
Ruicheng Wang
Jialiang Zhang
Jiayi Chen
Yinzhen Xu
Puhao Li
Tengyu Liu
He Wang
87
120
0
06 Oct 2022
Learning Diverse and Physically Feasible Dexterous Grasps with Generative Model and Bilevel Optimization
Learning Diverse and Physically Feasible Dexterous Grasps with Generative Model and Bilevel Optimization
A. Wu
Michelle Guo
Chenxi Liu
77
30
0
01 Jul 2022
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
Thomas Müller
Alex Evans
Christoph Schied
A. Keller
280
3,970
0
16 Jan 2022
Data-Driven Grasp Synthesis - A Survey
Data-Driven Grasp Synthesis - A Survey
Jeannette Bohg
A. Morales
Tamim Asfour
Danica Kragic
66
1,030
0
10 Sep 2013