04 June 2025
Inspired by the dynamic coupling of moto-neurons and physical elasticity in animals, EMERGE partners from the Delft University of Technology explore in this work the possibility of generating locomotion gaits by utilising physical oscillations in a soft snake by means of a low-level spiking neural mechanism.
To achieve this goal, they introduce the Double Threshold Spiking neuron model with adjustable thresholds to generate varied output patterns. This neuron model can excite the natural dynamics of soft robotic snakes, and it enables distinct movements, such as turning or moving forward, by simply altering the neural thresholds. Finally, they demonstrate that the approach, termed SpikingSoft, naturally pairs and integrates with reinforcement learning. Simulation results demonstrate that the proposed architecture significantly enhances the performance of the soft snake robot, enabling it to achieve target objectives with a 21.6% increase in success rate, a 29% reduction in time to reach the target, and smoother movements compared to the vanilla reinforcement learning controllers or Central Pattern Generator controller acting in torque space.
Read the paper in the link below.
More Information
