Home/No-Regret Replanning under Uncertainty

No-Regret Replanning under Uncertainty

Wen Sun, Niteesh Sood, Debadeepta Dey, Gireeja Ranade, Siddharth Prakash and Ashish Kapoor
Tech. Report, CMU-RI-TR-16-64, Robotics Institute, Carnegie Mellon University, arXiv, September, 2016

View Publication

Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.


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.

author = {Wen Sun and Niteesh Sood and Debadeepta Dey and Gireeja Ranade and Siddharth Prakash and Ashish Kapoor},
title = {No-Regret Replanning under Uncertainty},
year = {2016},
month = {September},
institution = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-16-64},
} 2017-09-13T10:38:16-04:00