Human softness perception in haptics has mainly been studied using mechanically homogeneous objects, despite the fact that many real-world objects exhibit heterogeneous layered structures with nonuniform stiffness. This study examined how layered heterogeneity modulates haptic softness perception. Sixteen lattice-structured stimuli were fabricated by 3D printing, with the stiffness of the upper four layers systematically varied while the bottom two layers remained fixed. Twenty-two participants evaluated the softness of the stimuli in a psychophysical task, and compression tests were conducted to quantify their mechanical properties. Perceived softness was significantly predicted by displacement under load, however, perceptual ranking did not fully coincide with the physical ranking. Linear mixed-effects analyses showed that the softness of the outermost layer had the greatest impact on the perceived softness. Perceived softness also increased as the number of soft subsurface layers increased, although this contribution decreased with depth. Layers 2 and 3 showed significant effects, whereas Layer 4 did not. These findings suggest that haptic softness perception depends not only on the overall stiffness but also on the depth-dependent distribution of compliance within layered structures.
Object compliance perception enables the identification of soft materials, supporting tasks such as fruit detection and assisted medical palpation. Compliance perception requires sensing an object's deformation and contact forces. Existing vision-based tactile sensing for compliance perception usually depends only on force or deformation. However, deformation-based approaches cannot reliably quantify object compliance, while force-based methods are overly sensitive to geometric variations. To address these limitations, this paper presents a framework that fuses temporal force sequences and deformation field information. Specifically, time-varying contact forces are inferred from tactile image sequences via a neural network, while deformation characteristics are encoded using depth maps. Crucially, the temporal force sequence is used as a dynamic force-response feature for compliance recognition. In standard experiments, the force prediction error reaches 0.06 N within a measurement range of 12 N. The proposed method achieves a 98.0% Shore-hardness classification accuracy for samples ranging from 10 HA to 80 HA. In practical scenarios, including abnormal fruit detection and soft matter, the overall accuracy exceeds 98.5%. This method improves the compliance perception ability of artificial tactile systems and facilitates their deployment in embodied perception applications.
Realistic palpation training requires reliable rendering of soft tissue stiffness changes in real time, which is difficult to achieve with conventional simulators. This paper presents a compact, variable-stiffness tactile interface (VSTaI) based on vacuum-induced jamming of 3D-printed structured fabrics. A vacuum-sealed fabric layer is sandwiched between two silicone layers, and stiffness is tuned by regulating internal pressure. Four fabric patterns with different geometric parameters were fabricated and evaluated using force-indentation tests under atmospheric and vacuum conditions. Across the tested pattern and geometry combinations, vacuum jamming increased stiffness significantly, producing an effective modulus from sub-megapascal to megapascal levels. Specifically, one configuration exhibited a stiffness increase of up to 140% under the jammed state. Circular chainmail patterns provided the most spatially uniform distribution of tactile stiffness, while denser geometries reached higher peak stiffness. VSTaI was also shown to exhibit excellent conformability to the underlying geometry. These results support structured-fabric jamming as a practical approach for shape-conformable, tunable-stiffness displays aimed at physical examination training.
Human tactile perception of materials relies on complex multisensory touch cues, yet the relationship between low-level tactile signals and perceptual representations remains poorly understood. This knowledge gap hinders the integration of touch in digital environments and the development of robots capable of human-like tactile perception. Here, we present an interpretable computational framework for modeling human material perception and recognition using multisensory touch data. Our framework comprises three interconnected models: Model 1 maps finger-surface interaction features to psychophysical sensory attributes, Model 2 classifies materials based on these perceptual representations, and Model 3 directly classifies materials from tactile features. The results showed that combining information from pressing, static contact, and sliding interactions improves prediction accuracy, and that thermal cues are particularly informative for both perceptual modeling and material classification. These findings highlight the importance of thermal and compliance cues, which remain underrepresented in current robotic fingers and haptic displays. Incorporating such cues may enhance artificial systems' ability to approximate human material perception and guide the design of more perceptually grounded haptic interfaces.