机器人通过多模态触觉感知软物属性，提升灵巧操作能力。
Embodied Tactile Perception of Soft Objects Properties
- 用可调机械性能的电子皮肤和多种触觉信号研究感知机制。
- 多模态传感比单一模态更优，交互策略影响感知精度。
- 提出隐式滤波器模型，可解释性地还原物体机械属性。
为使机器人具备类人精细操作能力，需理解机械柔顺性、多模态感知与有目的交互如何共同塑造触觉感知。本研究采用具备可调机械柔顺性和多模态传感（法向力、剪切力、振动）的专用模块化电子皮肤，系统探究感知具身化与交互策略对机器人感知物体的影响。基于一组具有可控粘弹性与表面特性的软波物体，我们测试了压、旋、滑等丰富触诊动作，变化压入深度、频率与方向。此外，提出一种无监督、动作条件的深度状态空间模型——隐式滤波器，用于建模复杂交互动态，并将因果机械属性映射至结构化隐空间。该方法提供可泛化且可解释的表征，揭示了环境与电子皮肤机械特性间的微妙互动，强调必须结合时间动态分析交互过程。实验表明，多模态传感显著优于单模态传感。
原文摘要 · Abstract (English)
To enable robots to develop human-like fine manipulation, it is essential to understand how mechanical compliance, multi-modal sensing, and purposeful interaction jointly shape tactile perception. In this study, we use a dedicated modular e-Skin with tunable mechanical compliance and multi-modal sensing (normal, shear forces and vibrations) to systematically investigate how sensing embodiment and interaction strategies influence robotic perception of objects. Leveraging a curated set of soft wave objects with controlled viscoelastic and surface properties, we explore a rich set of palpation primitives-pressing, precession, sliding that vary indentation depth, frequency, and directionality. In addition, we propose the latent filter, an unsupervised, action-conditioned deep state-space model of the sophisticated interaction dynamics and infer causal mechanical properties into a structured latent space. This provides generalizable and in-depth interpretable representation of how embodiment and interaction determine and influence perception. Our investigation demonstrates that multi-modal sensing outperforms uni-modal sensing. It highlights a nuanced interaction between the environment and mechanical properties of e-Skin, which should be examined alongside the interaction by incorporating temporal dynamics.
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