Dec 6, 2022 · In this work, we use contrastive learning to solve the shape control problem of deformable objects. The method jointly optimizes the visual representation ...
In this work, we use contrastive learning to solve the shape control problem of deformable objects. The method jointly optimizes the visual representation model ...
The method jointly optimizes the visual representation model and dynamic model of deformable objects, maps the target nonlinear state to linear latent space ...
Oct 22, 2024 · In this work, we use contrastive learning to solve the shape control problem of deformable objects. The method jointly optimizes the visual ...
Apr 25, 2021 · This work aims to learn latent Graph dynamics for DefOrmable Object Manipulation (G-DOOM). G-DOOM approximates a deformable object as a sparse set of ...
Missing: Autonomous | Show results with:Autonomous
In this paper, we propose a framework for learning a differentiable data-driven model for DLO dynamics with a minimal set of real-world data.
This work aims to learn latent Graph dynamics for DefOrmable Object Manipulation (GDOOM), which approximates a deformable object as a sparse set of ...
May 26, 2021 · Title: Learning Latent Graph Dynamics for Deformable Object Manipulation - Authors: Xiao Ma (National University of Singapore); ...
Missing: Autonomous Shape Control
Learning-based Feedback Controller for Deformable Object Manipulation
bohrium.dp.tech › paper › arxiv
Abstract:In this paper, we present a general learning-based framework to automatically visual-servo control the position and shape of a deformable object ...
This work aims to learn latent Graph dynamics for DefOrmable Object Manipulation (G-DOOM). To tackle the challenge of many DoFs and complex dynamics, G-DOOM ...