Manipulation Planning for Deformable Linear Objects

By Mitul Saha, Pekka Isto. 2007

188 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, 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 Computer Vision and Pattern Recognition

1. Lerrel Pinto
2. David Held
3. Sergey Levine
4. Pieter Abbeel
5. Luc Van Gool
6. Abhinav Kumar Gupta
7. Thanard Kurutach
8. Yufei Wang

In Control and Systems Engineering

1. Sergey Levine
2. Pieter Abbeel
3. Lerrel Pinto
4. Thanard Kurutach
5. David Held
6. Chelsea Finn
7. Xingyu Lin
8. Wilson Yan

Citations in the corpus

• SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation (2020, methodology): “Motion planning is then adopted along with analytical models of the deformable objects [23, 24].”
• Learning to Manipulate Deformable Objects without Demonstrations (2019, background): “Early work [49, 66, 39] focused on using planning for linearly deformable objects like ropes.”
• Flexible Object Manipulation (2010, background): “Saha, Isto, and Latombe developed a string model and motion planning algorithms for tying simulated knots [68, 70, 69].”