发表机构
Tsinghua Shenzhen International Graduate School, Tsinghua University; Z-Lab, Zerith Robotics; University of Toronto（清华大学深圳国际研究生院; Zerith机器人公司Z-Lab实验室; 多伦多大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Push-Wiper 框架将粘性污渍清洁转化为聚合问题，通过分段推动轨迹结合 Diffusion Policy 与 ASPI 控制器，清洁得分比基线高130%，可零样本泛化至多种污渍与表面。
AI 中文摘要
具有高粘度和复杂流变特性的粘性污渍仍是机器人表面清洁的主要挑战。传统擦拭常使污渍扩散，而 scrubbing（ scrubbing 保留原名）虽提供更强摩擦力，但存在损伤表面的风险。本文提出 Push-Wiper 框架，将粘性污渍清洁重新定义为聚合问题。Push-Wiper 采用海绵通过分段推动轨迹逐步收集污渍，随后进入后处理阶段分离聚合物质并实现海绵自清洁。我们采用分步策略进行污渍收集，并利用 Diffusion Policy（ Diffusion Policy 保留原名）生成自适应推动动作序列。这些序列通过我们的 Arbitrary Surface Pose Interpolator（ ASPI，保留原名）和混合力-位置控制器执行，使该方法能泛化到具有不同空间分布的污渍。Push-Wiper 的清洁得分（ CS，定义为已去除污渍面积的百分比）比基线方法最高高出 130%。无需额外训练，Push-Wiper 还能以零样本方式迁移到固体残渣、液体泼洒物、未见过的粘性污渍以及具有不同几何形状的曲面。我们的实验证明了 Push-Wiper 的清洁有效性及其强大的泛化能力，项目网站可访问该 https URL。
英文摘要
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/.
Comments8 pages, 8 figures. Accepted to the 2026 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2026)
