Skip to main content
Full-text links:


Current browse context:

new | recent | 1609

Change to browse by:

References & Citations


BibSonomy logo Mendeley logo Reddit logo ScienceWISE logo

Computer Science > Robotics

Title:No-Regret Replanning under Uncertainty

Abstract: This paper explores the problem of path planning under uncertainty. Specifically, we consider online receding horizon based planners that need to operate in a latent environment where the latent information can be modeled via Gaussian Processes. Online path planning in latent environments is challenging since the robot needs to explore the environment to get a more accurate model of latent information for better planning later and also achieves the task as quick as possible. We propose UCB style algorithms that are popular in the bandit settings and show how those analyses can be adapted to the online robotic path planning problems. The proposed algorithm trades-off exploration and exploitation in near-optimal manner and has appealing no-regret properties. We demonstrate the efficacy of the framework on the application of aircraft flight path planning when the winds are partially observed.
Comments: 8 pages
Subjects: Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:1609.05162 [cs.RO]
  (or arXiv:1609.05162v1 [cs.RO] for this version)

Submission history

From: Wen Sun [view email]
[v1] Fri, 16 Sep 2016 18:07:49 UTC (4,779 KB)