No-Regret Replanning under Uncertainty
This work addresses the challenge of efficient and adaptive path planning for robots in uncertain environments, though it is incremental as it adapts existing bandit methods to a new domain.
The paper tackles online path planning in latent environments by proposing a UCB-style algorithm that balances exploration and exploitation, achieving no-regret properties and demonstrating effectiveness in aircraft flight path planning with partially observed winds.
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.