用于多感官触觉数据材料感知与分类的可解释深度学习框架
An Interpretable Deep Learning Framework for Material Perception and Classification from Multisensory Tactile Data
浏览论文内容
中文总结 AI 辅助
该研究开发含三个深度学习模型的可解释框架，结合集成梯度实现高准确率与可解释性，揭示热线索具高信息价值，为触觉信号到材料感知提供计算解释。
中文摘要 AI 辅助
人类触觉感知依赖复杂的多感官线索，但触觉信号与感知表征间的关系仍未被充分理解，这限制了触觉在数字环境及类人机器人感知中的应用。为解决这一问题，我们开发了一个包含三个相互关联深度学习模型的计算框架，该框架将多感官触觉数据映射至材料感知，且不依赖手工特征。这些模型代表了从触觉信号到材料类别的不同路径：模型1从低级交互信号到感知属性分布，模型2从预测属性分布到材料分类，模型3直接从触觉信号到材料类别，绕过中间表征。通过结合深度学习与Integrated Gradients（集成梯度），该框架在实现高准确率的同时提供可解释性，揭示了哪些感官模态对其决策影响最大。结果显示，当不受中间感知阶段约束时，深度学习可达到近乎完美的材料分类性能，但在明确建模这些阶段后，要匹配类人表现则更具挑战。值得注意的是，热线索在所有模型中都表现出极高的信息价值，为材料区分提供了可靠信号。这些结果为触觉信号如何导向材料感知提供了计算层面的解释，并表明可解释深度学习既能达到类人水平的性能，又能揭示机器人与触觉系统需纳入的线索。
英文摘要
Human tactile perception relies on complex multisensory cues. Yet the relationship between tactile signals and perceptual representations remains poorly understood, limiting the integration of touch in digital environments and human-like robotic perception. To address this gap, we developed a computational framework comprising three interconnected deep learning models that map multisensory touch data to material perception, without relying on hand-crafted features. The models represent progressively different routes from tactile signals to material class: from low-level interaction signals to perceptual attribute distributions (Model 1), from predicted attribute distributions to material classification (Model 2), and directly from tactile signals to material categories, bypassing intermediate representations (Model 3). By combining deep learning with Integrated Gradients, the framework achieved high accuracy while offering interpretability, revealing which sensory modalities most strongly drive its decisions. Our results show that deep learning can approach near-perfect material classification when unconstrained by intermediate perceptual stages, but matching human-like performance is harder once those stages are modeled explicitly. Notably, thermal cues emerged as particularly informative across all models, providing robust signals for material differentiation. The results offer a computational account of how tactile signals lead to material perception and show how interpretable deep learning can both approach human-level performance and reveal cues that robotic and haptic systems need to incorporate.
发表机构
- Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。
