用双滤波器耦合提升机器人抓取与移动区域估计的精度和鲁棒性
Coupled Particle Filters for Robust Affordance Estimation
- 设计两个耦合递归滤波器，分别处理抓取与移动属性的不确定性
- 在真实数据集上精度比现有方法高245%~308%，低光/杂乱环境下仍稳定
- 适合需要精准、可靠物体交互理解的机器人应用
由于感知输入中存在视觉、几何和语义模糊性，机器人功能估计极具挑战。本文提出一种方法，通过两个耦合的递归估计算法，分别处理可抓取与可移动区域的子属性。每个估计算法编码特定属性的规律以降低不确定性，其耦合机制实现双向信息交换，聚焦于两者一致的区域，即功能区域。在真实世界数据集上的评估显示，该方法在精度上优于三种近期方法（Where2Act、Hands-as-Probes 和 HRP），分别提升308%、245%和257%；在低光照或杂乱环境中依然保持鲁棒性。此外，在真实场景测试中达到70%的成功率。结果表明，耦合互补估计算法可生成精确、鲁棒且符合机体特征的功能预测。
原文摘要 · Abstract (English)
Robotic affordance estimation is challenging due to visual, geometric, and semantic ambiguities in sensory input. We propose a method that disambiguates these signals using two coupled recursive estimators for sub-aspects of affordances: graspable and movable regions. Each estimator encodes property-specific regularities to reduce uncertainty, while their coupling enables bidirectional information exchange that focuses attention on regions where both agree, i.e., affordances. Evaluated on a real-world dataset, our method outperforms three recent affordance estimators (Where2Act, Hands-as-Probes, and HRP) by 308%, 245%, and 257% in precision, and remains robust under challenging conditions such as low light or cluttered environments. Furthermore, our method achieves a 70% success rate in our real-world evaluation. These results demonstrate that coupling complementary estimators yields precise, robust, and embodiment-appropriate affordance predictions.
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