Tactile sensing typically involves active exploration of unknown surfaces and objects, making it especially effective at processing the characteristics of materials and textures. A key property extracted by human tactile perception in material classification is surface roughness, which relies on measuring vibratory signals using the multi-layered fingertip structure. Existing robotic systems lack tactile sensors that are able to provide high dynamic sensing ranges, perceive material properties, and maintain a low hardware cost. In this work, we introduce the reference design and fabrication procedure of a miniature and low-cost tactile sensor consisting of a biomimetic cutaneous structure, including the artificial fingerprint, dermis, epidermis, and an embedded magnet-sensor structure which serves as a mechanoreceptor for converting mechanical information to digital signals. The presented sensor is capable of detecting high-resolution magnetic field data through the Hall effect and creating high-dimensional time-frequency domain features for material texture classification. Additionally, we investigate the effects of different superficial sensor fingerprint patterns for classifying materials through both simulation and physical experimentation. After extracting time series and frequency domain features, we assess a k-nearest neighbors classifier for distinguishing between different materials. The results from our experiments show that our biomimetic tactile sensors with fingerprint ridges can classify materials with more than 7.7% higher accuracy and lower variability than ridge-less sensors. These results, along with the low cost and customizability of our sensor, demonstrate high potential for lowering the barrier to entry for a wide array of robotic applications, including modelless tactile sensing for texture classification, material inspection, and object recognition.

Damping properties in biological muscle are crit-ical for absorbing shock, maintaining posture, and positioning limbs and appendages. When creating biomimetic robots, the ability to replicate the dynamics of biological muscle is neces-sary to reproduce behaviors seen in an animal model. However, the damping properties of existing soft artificial muscles are difficult to predict and tune to match specific muscles as may be needed in biomimetic robots. Here, we present the design, manufacturing, and characterization of a novel soft damper to enable a greater degree of biomimetism in these soft actuators. The damper is composed of magnetorheological fluid contained within an elastomeric shell, which is cast using low-cost 3D printed parts and commercially available urethane rubber. We demonstrate that the force-velocity response over a velocity range of 0.1 to 10 mm/s is proportional to applied magnetic flux densities between 0.12 and 0.31 T. In the presence of a 0.31 T magnetic field from a small permanent magnet, the damper is capable of a maximum damping force increase of 13.2 N to 15.5 N relative to the 0 T control, at a compression depth of 7.9 mm, which is larger than that of several previously reported centimeter-scale dampers. As a proof-of-concept for integration with a Pneumatic Artificial Muscle (PAM), we use two parallel dampers to reduce the oscillations of a rapidly pressurized McKibben actuator. The ability to modulate the force-velocity performance of our elastomeric damper paves the way for custom damping profiles that can be used to improve biomimetism in soft robotic actuators.