Name
Abstract | ||
|---|---|---|
Learning high-level navigation behaviors has important implications: it enables robots to build compact visual memory for repeating demonstrations and to build sparse topological maps for planning in novel environments. Existing approaches only learn discrete, short-horizon behaviors. These standalone behaviors usually assume a discrete action space with simple robot dynamics, thus they cannot capture the intricacy and complexity of real-world trajectories. To this end, we propose Composable Behavior Embedding (CBE), a continuous behavior representation for long-horizon visual navigation. CBE is learned in an end-to-end fashion; it effectively captures path geometry and is robust to unseen obstacles. We show that CBE can be used to performing memory-efficient path following and topological mapping, saving more than an order of magnitude of memory than behavior-less approaches. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/LRA.2021.3060649 | IEEE ROBOTICS AND AUTOMATION LETTERS |
Keywords | DocType | Volume |
Navigation, Visualization, Robots, Task analysis, Trajectory, Generators, Simultaneous localization and mapping, Deep learning for visual perception, learning from demonstration, vision-based navigation | Journal | 6 |
Issue | ISSN | Citations |
2 | 2377-3766 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiangyun Meng | 1 | 13 | 3.71 |
| Yu Xiang | 2 | 629 | 23.04 |
| Dieter Fox | 3 | 12306 | 1289.74 |