Adaptive Surface Cleaning under Uncertain Cleaning Response
Overview
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.
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Environment: simulation
“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.”
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- arXiv Robotics — research abstractsSignalAdaptive 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.
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