发表机构
University of Luxembourg; Eurecat, Centre Tecnològic de Catalunya（卢森堡大学; 加泰罗尼亚技术中心Eurecat）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出一种极简光流表示，经7×9网格聚合为126个特征，结合XGBoost模型，可跨地球、火星等四个重力域实现高精度触觉旋转分类，缩减特征后仍保持高准确率且推理速度快，为空间机器人触觉感知提供了域适应方案。
AI 中文摘要
基于视觉的触觉传感器能提供丰富的接触信息，但处理高分辨率图像对空间机器人等资源受限平台而言成本较高。本研究探究触觉运动的紧凑表示能否对不同重力条件下的物体旋转进行分类。从模拟GelSight Mini传感器获取的密集光流，经7×9网格聚合为126个特征，用于对地球、火星、月球及轨道重力下负载诱导的旋转方向进行分类。重力会导致这些特征发生微小但显著的偏移，占其方差的1.6%（R²=0.016）。尽管偏移幅度小，但仅在地球数据上训练的模型会受影响：XGBoost在地球的准确率为94.4%，在轨道环境降至75.9%。相比之下，在全部四个重力域上训练的单一模型，无需将重力作为输入，即可实现96.3%的总体准确率，且在各重力域的准确率为95.1%-97.0%。该表示还可缩减至40个特征，同时保留95.7%的准确率，XGBoost每次推理仅需0.14毫秒。这些发现表明，仅地球重力下的性能不足以确立空间机器人操作中触觉感知的可迁移性，凸显了在训练与验证阶段考虑重力诱导域偏移的必要性。
英文摘要
Vision-based tactile sensors provide rich contact information, but processing high-resolution images can be costly for resource-constrained platforms such as space robots. This work investigates whether a compact representation of tactile motion can classify object rotation across different gravity conditions. Dense optical flow from a simulated GelSight Mini is aggregated over a 7x9 grid into 126 features and used to classify the direction of load-induced rotation under Earth, Mars, Moon, and orbital gravity. Gravity causes a small but significant shift in these features, accounting for 1.6% of their variance (R2 = 0.016). Despite its small magnitude, this shift affects models trained only on Earth data: XGBoost accuracy decreases from 94.4% on Earth to 75.9% in orbit. In contrast, a single model trained across all four gravity domains achieves 96.3% overall accuracy and 95.1%-97.0% across individual domains, without using gravity as an input. The representation can also be reduced to 40 features while retaining 95.7% accuracy, with XGBoost requiring only 0.14 ms per inference. These findings show that Earth-gravity performance alone is insufficient to establish the transferability of tactile perception for space robotic manipulation, highlighting the need to account for gravity-induced domain shifts during training and validation.
