用脉冲神经元控制软体蛇形机器人，通过调节阈值实现自然运动。
SpikingSoft: A Spiking Neuron Controller for Bio-inspired Locomotion with Soft Snake Robots
- 设计可调阈值的双阈值脉冲神经元，激发机器人本体振荡
- 使机器人成功率提升21.6%，到达目标时间减少29%且动作更平滑
- 与强化学习天然结合，仅调两个参数即可生成复杂运动，适合控制新手
受动物运动神经元与物理弹性动态耦合启发，本文探索利用软体蛇形机器人的物理振荡，通过低层脉冲神经机制生成运动步态。为此，提出可调阈值的双阈值脉冲神经元模型，该模型能激发软体机器人的自然动力学，仅通过调整神经元阈值即可实现前进、转向等不同动作。最终，所提出的SpikingSoft方法可与强化学习自然融合：高层智能体仅需调节两个阈值，即可生成复杂运动模式，显著简化反应式运动的学习过程。仿真结果表明，该架构显著提升软体蛇形机器人的性能，相比基线强化学习控制器或扭矩空间下的中枢模式发生器，其在达成目标时成功率提升21.6%，到达目标时间减少29%，且运动更平滑。
原文摘要 · Abstract (English)
Inspired by the dynamic coupling of moto-neurons and physical elasticity in animals, this work explores the possibility of generating locomotion gaits by utilizing physical oscillations in a soft snake by means of a low-level spiking neural mechanism. To achieve this goal, we 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, we demonstrate that our approach, termed SpikingSoft, naturally pairs and integrates with reinforcement learning. The high-level agent only needs to adjust the two thresholds to generate complex movement patterns, thus strongly simplifying the learning of reactive locomotion. 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.
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