Towards programming tools for robots that integrate probabilistic computation and learning
By Sebastian Thrun. 2000
52 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computational Theory and Mathematics, Probabilistic & Bayesian Methods, Robotics & Control
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Sergey Levine
- Pieter Abbeel
- Andrew Y. Ng
- Zoubin Ghahramani
- Ruslan Salakhutdinov
- Yoshua Bengio
- H. F. Durrant-Whyte
- Abhinav Kumar Gupta
In Computational Theory and Mathematics
- Zoubin Ghahramani
- Andrew Y. Ng
- H. F. Durrant-Whyte
- Daphne Koller
- Yufeng Liu
- Sebastian Thrun
- Jiaxin Shi
- Jun Zhu
In Probabilistic & Bayesian Methods
- Zoubin Ghahramani
- Andrew Y. Ng
- H. F. Durrant-Whyte
- Daphne Koller
- Yufeng Liu
- Sebastian Thrun
- David M. Blei
- Yarin Gal
In Robotics & Control
- Andrew Y. Ng
- H. F. Durrant-Whyte
- Daphne Koller
- Zoubin Ghahramani
- Yufeng Liu
- Sebastian Thrun
- Ashutosh Saxena
- Sung Ju Hwang
Citations in the corpus
- An Introduction to Probabilistic Programming (2018, neutral): “, 1997), (Thrun, 2000); and statistics: Biips (Todeschini et al.”
- Simultaneous Localization and Mapping with Sparse Extended Information Filters (2004, methodology): “…al. / Simultaneous Localization and Mapping 707 In our simulations, we focused particularly on the " loop closing " problem, which is generally acknowledged to be one of the hardest problems in SLAM (Lu and Milios 1997; Gutmann and Konolige 1999; Thrun 2000; Bosse et al. 2003; Hähnel et al. 2003a).”