Neural Autonomous Navigation with Riemannian Motion Policy
By Xiangyun Meng, Nathan D. Ratliff, Yu Xiang, Dieter Fox. 2019
39 citations recorded by the source corpus.
End-to-end learning for autonomous navigation has received substantial attention recently as a promising method for reducing modeling error. However, its data complexity, especially around generalization to unseen environments, is high. We introduce a novel image-based autonomous navigation technique that leverages in policy structure using the Riemannian Motion Policy (RMP) framework for deep learning of vehicular control. We design a deep neural network to predict control point RMPs of the vehicle from visual images, from which the optimal control commands can be computed analytically. We show that our network trained in the Gibson environment can be used for indoor obstacle avoidance and navigation on a real RC car, and our RMP representation generalizes better to unseen environments than predicting local geometry or predicting control commands directly.
Subfields: Automotive 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 Automotive Engineering
- Pieter Abbeel
- Sergey Levine
- Jitendra Malik
- Gregory Kahn
- Benjamin Eysenbach
- Ofir Nachum
- Shixiang Shane Gu
- Chelsea Finn
In Computer Vision and Pattern Recognition
- Jitendra Malik
- Sergey Levine
- Luc Van Gool
- Abhinav Kumar Gupta
- Trevor Darrell
- Dacheng Tao
- Xiaogang Wang
- Andrew Zisserman
Citations in the corpus
- BADGR: An Autonomous Self-Supervised Learning-Based Navigation System (2020, background): “However, these methods have typically focused on pure geometric reasoning, require on-policy data, and often utilize simulation due to constraints such as sample efficiency [28, 8, 29, 30, 31, 32].”
- Combining Optimal Control and Learning for Visual Navigation in Novel Environments (2019, background): “[35] combine Riemannian Motion Policy with learning for autonomous navigation, whereas we focus on dynamically feasible spline-based policies.”