The Synthetic Nervous System (SNS) is a biologically inspired neural network (NN). Due to its capability of capturing complex mechanisms underlying neural computation, an SNS model is a candidate for building compact and interpretable NN controllers for robots. Previous work on SNSs has focused on applying the model to the control of legged robots and the design of functional subnetworks (FSNs) to realize dynamical systems. However, the FSN approach has previously relied on the analytical solution of the governing equations, which is difficult for designing more complex NN controllers. Incorporating plasticity into SNSs and using learning algorithms to tune the parameters offers a promising solution for systematic design in this situation. In this paper, we theoretically analyze the computational advantages of SNSs compared with other classical artificial neural networks. We then use learning algorithms to develop compact subnetworks for implementing addition, subtraction, division, and multiplication. We also combine the learning-based methodology with a bioinspired architecture to design an interpretable SNS for the pick-and-place control of a simulated gantry system. Finally, we show that the SNS controller is successfully transferred to a real-world robotic platform without further tuning of the parameters, verifying the effectiveness of our approach.

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.

Current techniques in robot design and fabrication are time consuming and costly. Robot designs are needed that facilitate low-cost fabrication techniques and reduce the design to production timeline. Here we present an axial-rotational coupled metastructure that can serve as the functional core of a low-cost 3D printed walking robot. Using an origami-inspired assembly technique, the axial-rotational coupled metastructure robot can be 3D printed flat and then folded into a final configuration. This print-then-fold approach allows for the facile integration of critical subcomponents during the printing process. The axial-rotational metastructures eliminate the need for joints and linkages by enabling locomotion through a single compliant structure. Finite element models of the axialrotational metastructures were developed and validated against experimental deformation of 3D printed units under tensile loading. As a proof-of-concept, an ultra low-cost 3D-printed metabot was designed and fabricated using the proposed axial-rotational coupled metastructure and its walking performance was characterized. A top speed of 4.30 mm/s was achieved with an alternating stepping gait at a frequency of 0.8 Hz.

Cockroach shelter-seeking strategy may look like an undirected random search, but we show that they are attracted to darkened shelters, arriving at a shelter in about half the time it would otherwise take. We were able to identify four statistically significant trends from the behavior of 134 cockroaches in one-minute naïve walking trials with four different arena configurations. By combining these trends into a model, we arrive at an algorithm that significantly directs a simulated agent to a location. This algorithm was then adapted and tested on a small mobile robot equipped with an onboard camera and antenna-like contact sensors.