With their unique combination of characteristics - an energy density almost 100 times that of human muscle, and a power density of 5.3 kW/kg, similar to a jet engine's output - Nylon artificial muscles stand out as particularly apt for robotics applications. However, the necessity of integrating sensors and controllers poses a limitation to their practical usage. Here we report a constant power open-loop controller based on machine learning. We show that we can control the position of a nylon artificial muscle without external sensors. To this end, we construct a mapping from a desired displacement trajectory to a required power using an ensemble encoder-style feed-forward neural network. The neural controller is carefully trained on a physics-based denoised dataset and can be fine-tuned to accommodate various types of thermal artificial muscles, irrespective of the presence or absence of hysteresis.

翻译：凭借其独特的属性组合——能量密度约为人体肌肉的100倍，功率密度达5.3 kW/kg，可与喷气发动机的输出功率相媲美——尼龙人造肌肉在机器人应用中展现出尤为突出的适用性。然而，集成传感器与控制器的必要性对其实际应用构成了限制。本文报道了一种基于机器学习的恒功率开环控制器。我们证明，无需外部传感器即可实现对尼龙人造肌肉位置的控制。为此，我们采用集成编码器式前馈神经网络，构建了从期望位移轨迹到所需功率的映射关系。该神经控制器在基于物理降噪的数据集上经过精心训练，并可通过微调适配各种类型的热致动器，无论其是否存在迟滞效应。