Soft Material Modeling for Robotic Manipulation
By Nabil Essahbi, Belhassen-Chedli Bouzgarrou, Grigore Gogu. 2012
29 citations recorded by the source corpus.
This article presents a new approach of soft material modeling for robotic manipulation which combines physical modeling and tracking of a deformable object. We discuss the construction of geometrical models using MRI system and how to overcome the problem of variability. The physical model focuses on meat/muscles deformation. We introduce the principal criteria for choosing most appropriate models and discuss two modeling methods which suit our problem: mass spring model and tensor mass model. The introduction of anisotropy in these models allows results to be more realistic but evolves an increasing of computing time.
Subfields: Computational Mechanics, Control and Systems Engineering
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
In Control and Systems Engineering
- Sergey Levine
- Pieter Abbeel
- Lerrel Pinto
- Thanard Kurutach
- David Held
- Chelsea Finn
- Xingyu Lin
- Wilson Yan
Citations in the corpus
- SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation (2020, background): “However, these planning approaches often suffer from the large configuration space induced by the high degree of freedom of the deformable objects [25].”
- Learning to Manipulate Deformable Objects without Demonstrations (2019, background): “This, coupled with the complex dynamics [7], has prompted work in using high-level planners or demonstrations and local controllers to follow the plans.”