No-Regret Replanning under Uncertainty

TL;DR

This paper introduces a no-regret online path planning algorithm for environments with uncertain latent information, modeled via Gaussian Processes, balancing exploration and exploitation effectively.

Contribution

It adapts UCB bandit algorithms to robotic path planning under uncertainty, providing a theoretically grounded approach with proven no-regret guarantees.

Findings

Effective in aircraft flight path planning with partial wind observations

Balances exploration and exploitation near-optimally

Demonstrates theoretical no-regret properties

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.