An obstacle-based rapidly-exploring random tree
By Samuel Rodríguez, Xinyu Tang, Jyh-Ming Lien, Nancy M. Amato. 2006
233 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Biomedical Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Lerrel Pinto
- David Held
- Thanard Kurutach
- M. Simic
- Pieter Abbeel
- Xingyu Lin
- Yilin Wu
- Mohamed Elbanhawi
In Biomedical Engineering
- Pieter Abbeel
- David Held
- Thanard Kurutach
- Lerrel Pinto
- Sergey Levine
- M. Simic
- Mohamed Elbanhawi
- Chelsea Finn
In Computer Vision and Pattern Recognition
- Lerrel Pinto
- David Held
- Sergey Levine
- Pieter Abbeel
- Luc Van Gool
- Abhinav Kumar Gupta
- Thanard Kurutach
- Yufei Wang
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, methodology): “[31] developed methods for fully deformable simulation environments, while [32] created methods for faster planning with deformable environments.”
- Sampling-Based Robot Motion Planning: A Review (2014, background): “Obstacle Based RRT (OB-RRT) gathers data from obstacles and selects predetermined growth directions [101].”