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The ability to render realistic texture perception using haptic devices has been consistently challenging. A key component of texture perception is roughness. When we touch surfaces, mechanoreceptors present under the skin are activated and the information is processed by the nervous system, enabling perception of roughness/smoothness. Several distributed haptic devices capable of producing localized skin stretch have been developed with the aim of rendering realistic roughness perception; however, current state-of-the-art devices rely on device fabrication and psychophysical experimentation to determine whether a device configuration will perform as desired. Predictive models can elucidate physical mechanisms, providing insight and a more effective design iteration process. Since existing models (1, 2) are derived from responses to normal stimuli only, they cannot predict the performance of laterally actuated devices which rely on frictional shear forces to produce localized skin stretch. They are also unable to predict the augmentation of roughness perception when the actuators are spatially dispersed across the contact patch or actuated with a relative phase difference (3). In this study, we have developed a model that can predict the perceived roughness for arbitrary external stimuli and validated it against psychophysical experimental results from different haptic devices reported in the literature. The model elucidates two key mechanisms: (i) the variation in the change of strain across the contact patch can predict roughness perception with strong correlation and (ii) the inclusion of lateral shear forces is essential to correctly predict roughness perception. Using the model can accelerate device optimization by obviating the reliance on trial-and-error approaches.

Clutches are integral components in robotic systems, enabling programming of system stiffness and precise control over a wide range of motion types. While different types of clutches exist, electroadhesive (EA) clutches present several key advantages, such as flexibility, low mass, low power consumption, simplicity, and fast response. Achieving high EA stress in EA clutches has remained a challenge, however, necessitating high voltage input or a large contact area to achieve the desired force. In this work, an EA clutch is proposed with a high EA stress achieved by taking fracture mechanics into account and using a high dielectric composite layer while still maintaining a comparable high switching speed to other dielectric‐based EA clutches. The maximum EA stress is observed to be 108.8 N cm⁻², which is four times larger than what has been reported previously among dielectric‐based EA clutches at room temperature. This high EA stress clutch can facilitate miniaturization and lower the operating voltage as well as extend to high load capacity applications. The proposed approach holds promise for advancements in various domains, including haptics (both kinesthetic and cutaneous), exoskeletons, walking robots, and other systems that require compliance, low mass, and precise force control.

Currently soft robots primarily rely on pneumatics and geometrical asymmetry to achieve locomotion, which limits their working range, versatility, and other untethered functionalities. In this paper, we introduce a novel approach to achieve locomotion for soft robots through dynamically tunable friction to address these challenges, which is achieved by subsurface stiffness modulation (SSM) of a stimuli-responsive component within composite structures. To demonstrate this, we design and fabricate an elastomeric pad made of polydimethylsiloxane (PDMS), which is embedded with a spiral channel filled with a low melting point alloy (LMPA). Once the LMPA strip is melted upon Joule heating, the compliance of the composite structure increases and the friction between the composite surface and the opposing surface increases. A series of experiments and finite element analysis (FEA) have been performed to characterize the frictional behavior of these composite pads and elucidate the underlying physics dominating the tunable friction. We also demonstrate that when these composite structures are properly integrated into soft crawling robots inspired by inchworms and earthworms, the differences in friction of the two ends of these robots through SSM can potentially be used to generate translational locomotion for untethered crawling robots.

Controlling adhesion on demand is essential for many manufacturing and assembly processes such as microtransfer printing. Among various strategies, pneumatics‐controlled switchable adhesion is efficient and robust but currently still suffers from challenges in miniaturization and high energy cost. In this paper, a novel way to achieve tunable adhesion using low pressure by inducing sidewall buckling in soft hollow pillars (SHPs) is introduced. It is shown that the dry adhesion of these SHPs can be changed by more than two orders of magnitude (up to 151×) using low activating pressure (≈−10 or ≈20 kPa). Large enough negative pressure triggers sidewall buckling while positive pressure induces sidewall bulging, both of which can significantly change stress distribution at the bottom surface to facilitate crack initiation and reduce adhesion therein. It is shown that a single SHP can be activated by a micropump to manipulate various lightweight objects with different curvatures and surface textures. Here, it is also demonstrated that an array of SHPs can realize selective pick‐and‐place of an array of objects. These demonstrations illustrate the robustness, simplicity, and versatility of these SHPs with highly tunable dry adhesion.