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Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning

Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning

C. Acar
K. P. Tee
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Papers citing "Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning"

2 / 2 papers shown
Title
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference
Constrained Gaussian Process Motion Planning via Stein Variational Newton Inference
Jiayun Li
Kay Pompetzki
An T. Le
Haolei Tong
Jan Peters
Georgia Chalvatzaki
33
0
0
07 Apr 2025
NODE IK: Solving Inverse Kinematics with Neural Ordinary Differential Equations for Path Planning
NODE IK: Solving Inverse Kinematics with Neural Ordinary Differential Equations for Path Planning
Suhan Park
M. Schwartz
Jaeheung Park
11
6
0
01 Sep 2022