Adaptive Surface Cleaning under Uncertain Cleaning Response
Adaptive Surface Cleaning under Uncertain Cleaning Response
This research abstract introduces a method for robots to adaptively clean surfaces where the effectiveness of cleaning varies spatially and is initially unobservable. The authors formulate the problem as a partially observable Markov decision process (POMDP) and propose a belief-aware planning framework that updates a latent local cleaning-response model based on observed outcomes. This allows the robot to adapt its cleaning motions, forces, and passes in real-time. Preliminary simulations in grid-world environments demonstrate that the closed-loop planner can successfully adjust its spatial decomposition and cleaning actions as the surface condition evolves.
The paper proposes a belief-aware planning framework for adaptive surface cleaning that treats contamination response as partially observable, using observed outcomes to update latent models and adjust subsequent motions.
Source: arXiv Robotics — research abstracts · Read original article ↗
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update a latent local cleaning-response model and adapt subsequent cleaning motions, forces, and passes. Preliminary grid-world simulations demonstrate the resulting closed-loop planner adapting its spatial decomposition and cleaning actions as the surface evolves.
Open source S3
arXiv:2611.01437v1 Announce Type: new Abstract: Surface cleaning requires robots to adapt both where they clean and how they interact with contamination whose response to cleaning may vary spatially and remain unobservable prior to interaction. We formulate adaptive surface cleaning as a partially observable Markov decision process and propose a belief-aware planning framework that uses observed cleaning outcomes to
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Source：arXiv Robotics — research abstracts · arxiv.org