When humans touch an object with their fingertips, they can immediately describe its tactile properties using haptic adjectives, such as hardness and roughness; however, human perception is subjective and noisy, with significant variation across individuals and interactions. Recent research has worked to provide robots with similar haptic intelligence but was focused on identifying binary haptic adjectives, ignoring both attribute intensity and perceptual variability. Combining ordinal haptic adjective labels gathered from human subjects for a set of 60 objects with features automatically extracted from raw multi-modal tactile data collected by a robot repeatedly touching the same objects, we designed a machine-learning method that incorporates partial knowledge of the distribution of object labels into training; then, from a single interaction, it predicts a probability distribution over the set of ordinal labels. In addition to analyzing the collected labels (10 basic haptic adjectives) and demonstrating the quality of our method's predictions, we hold out specific features to determine the influence of individual sensor modalities on the predictive performance for each adjective. Our results demonstrate the feasibility of modeling both the intensity and the variation of haptic perception, two crucial yet previously neglected components of human haptic perception.

中文翻译：

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学习预测触觉形容词的知觉分布。

当人们用指尖触摸对象时，他们可以立即使用触觉形容词（例如硬度和粗糙度）来描述其触觉特性。然而，人类的感知是主观和嘈杂的，个体之间和交互之间的差异很大。最近的研究致力于为机器人提供类似的触觉智能，但专注于识别二元触觉形容词，而忽略了属性强度和感知可变性。结合从人类对象收集的一组60个对象的序数触觉形容词标签与从机器人反复触摸相同对象而收集的原始多模式触觉数据中自动提取的特征，我们设计了一种包含部分分布知识的机器学习方法训练对象标签；然后，通过一次互动，它可以预测顺序标签集上的概率分布。除了分析收集的标签（10个基本的触觉形容词）并演示我们方法的预测质量外，我们还提供特定功能来确定各个传感器模式对每个形容词的预测性能的影响。我们的研究结果证明了对触觉感知的强度和变化进行建模的可行性，触觉感知是人类触觉感知的两个至关重要但以前被忽略的组成部分。我们采用特定的功能来确定各个传感器模式对每个形容词的预测性能的影响。我们的结果证明了对人类触觉感知的两个关键但先前被忽略的触觉感知的强度和变化进行建模的可行性。我们采用特定的功能来确定各个传感器模式对每个形容词的预测性能的影响。我们的结果证明了对人类触觉感知的两个关键但先前被忽略的触觉感知的强度和变化进行建模的可行性。