用神经储备池控制仿生软体机械臂，实现高效自建模与节能运行。
Neural reservoir control of a soft bio-hybrid arm
- 采用神经储备池实现多肌肉群协同控制与自我建模
- 在复杂任务中优于传统神经网络，能耗降低近两个数量级
- 适合小型无线软体机器人实时控制，具硬件部署潜力
长期以来，软体机器人的控制难题源于其高度非线性、异质性、各向异性和分布特性。本文结合工程与生物学思路，利用神经储备池对由多个肌腱组包裹弹性脊柱构成的生物混合机械臂进行动态控制。结果表明，储备池可同时实现复杂任务下的协同控制与自我建模，性能优于经典神经网络方法。进一步地，在类脑硬件上实现脉冲储备池，能耗较标准CPU降低近两个数量级，为小型无缆软体机器人的机载控制提供了可行路径。
原文摘要 · Abstract (English)
A long-standing engineering problem, the control of soft robots is difficult because of their highly non-linear, heterogeneous, anisotropic, and distributed nature. Here, bridging engineering and biology, a neural reservoir is employed for the dynamic control of a bio-hybrid model arm made of multiple muscle-tendon groups enveloping an elastic spine. We show how the use of reservoirs facilitates simultaneous control and self-modeling across a set of challenging tasks, outperforming classic neural network approaches. Further, by implementing a spiking reservoir on neuromorphic hardware, energy efficiency is achieved, with nearly two-orders of magnitude improvement relative to standard CPUs, with implications for the on-board control of untethered, small-scale soft robots.
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