2 citations · 2 across the 2 of their papers we have counts for
3 papers
cs.RO2021
Learning Composable Behavior Embeddings for Long-horizon Visual Navigation
Xiangyun Meng, Yu Xiang, Dieter Fox
Learning high-level navigation behaviors has important implications: it enables robots to build compact visual memory for repeating demonstrations and to build sparse topological m…
cs.RO2019
Scaling Local Control to Large-Scale Topological Navigation
Xiangyun Meng, Nathan Ratliff, Yu Xiang +1
Visual topological navigation has been revitalized recently thanks to the advancement of deep learning that substantially improves robot perception. However, the scalability and re…
cs.RO2019★ 2 cited
Neural Autonomous Navigation with Riemannian Motion Policy
Xiangyun Meng, Nathan Ratliff, Yu Xiang +1
End-to-end learning for autonomous navigation has received substantial attention recently as a promising method for reducing modeling error. However, its data complexity, especiall…