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.

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.