Name
Abstract | ||
|---|---|---|
We propose a method to approximate the distribution of robot configurations satisfying multiple objectives. Our approach uses Variational Inference, a popular method in Bayesian computation, which has several advantages over sampling-based techniques. To be able to represent the complex and multimodal distribution of configurations, we propose to use a mixture model as approximate distribution, an approach that has gained popularity recently. In this work, we show the interesting properties of this approach and how it can be applied to a range of problems. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Robotics | Journal |
Volume | Citations | PageRank |
abs/1905.09597 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emmanuel Pignat | 1 | 12 | 4.00 |
| Teguh Lembono | 2 | 1 | 0.36 |
| Sylvain Calinon | 3 | 1897 | 117.63 |