Search papers, labs, and topics across Lattice.
This paper introduces Push-Wiper, a novel robotic cleaning framework that addresses the challenges of removing viscous stains through a segmented pushing trajectory approach. By reformulating the cleaning process as an aggregation problem, Push-Wiper utilizes a sponge to gather stains progressively, followed by a post-processing phase for material detachment and sponge self-cleaning. The method achieves a cleaning score up to 130% higher than existing techniques and demonstrates zero-shot transferability to various stain types and surface geometries without additional training.
Push-Wiper's innovative approach to robotic cleaning can remove up to 130% more stains than traditional methods while adapting seamlessly to different surfaces and stain types.
Viscous stains, characterized by high viscosity and complex rheological properties, remain a major challenge for robotic surface cleaning. Conventional wiping often spreads the stain, while scrubbing provides stronger friction but risks damaging the surface. In this paper, we propose Push-Wiper, a framework that reformulates viscous stain cleaning as an aggregation problem. Push-Wiper employs a sponge to progressively gather stains through segmented pushing trajectories, followed by a post-processing phase that detaches the aggregated material and enables sponge self-cleaning. We adopt a stepwise strategy for stain gathering and leverage Diffusion Policy to generate adaptive pushing action sequences. These sequences are executed through our Arbitrary Surface Pose Interpolator (ASPI) and a hybrid force-position controller, allowing the method to generalize to stains with diverse spatial distributions. Push-Wiper achieves a cleaning score (CS), defined as the percentage of stain area removed, up to 130% higher than baseline methods. Without additional training, Push-Wiper also transfers in a zero-shot manner to solid residues, liquid spills, unseen viscous stains, and curved surfaces with varying geometries. Our experiments demonstrate the cleaning effectiveness of Push-Wiper and its strong generalization ability. The project website is available at https://push-wiper.github.io/.
