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About

Alex Spaeth is a robotics researcher whose work lies at the intersection of bio-inspired control, soft robotics, and modular design. His most significant contributions center on neuromorphic closed-loop control, where he has pioneered the use of spiking neural networks to create robust, biomimetic controllers for flexible robots. His 2020 paper on a spiking neural state machine for gait entrainment (20 citations) introduced a modular architecture of bistable relaxation oscillators, demonstrating remarkable resilience to parameter variation—a key challenge in soft robotics. Spaeth further advanced this paradigm with a simulated spiking central pattern generator (CPG) for hierarchical control (14 citations), using just twelve neurons to achieve sensory-modulated locomotion in a flexible modular robot. Beyond neural control, Spaeth has explored origami-inspired design, developing self-locking rotational joints that improve compactness and manufacturability (5 citations, 2023). He has also contributed to the kinematics of voxel-based soft robots, using finite element methods to characterize deformation-driven motion (3 citations, 2022). Spaeth’s work uniquely bridges theoretical neuroscience and practical robotics, offering scalable, energy-efficient solutions for adaptive locomotion in unstructured environments.