Recent advances unveiled physical neural networks as promising machine learning platforms, offering faster and more energy-efficient information processing. Compared with extensively-studied optical neural networks, the development of mechanical neural networks (MNNs) remains nascent and faces significant challenges, including heavy computational demands and learning with approximate gradients. Here, we introduce the mechanical analogue of in situ backpropagation to enable highly efficient training of MNNs. We demonstrate that the exact gradient can be obtained locally in MNNs, enabling learning through their immediate vicinity. With the gradient information, we showcase the successful training of MNNs for behavior learning and machine learning tasks, achieving high accuracy in regression and classification. Furthermore, we present the retrainability of MNNs involving task-switching and damage, demonstrating the resilience. Our findings, which integrate the theory for training MNNs and experimental and numerical validations, pave the way for mechanical machine learning hardware and autonomous self-learning material systems.

我们介绍了一种高效的机械神经网络(MNN)训练方法，该方法通过机械模拟的现场反向传播实现了在MNN中获取准确梯度信息的能力，从而成功地训练了MNN并实现了高精度的回归和分类任务，同时展示了MNN的重训练性和鲁棒性。我们的研究结果为机械机器学习硬件和自主自学材料系统铺平了道路。

透过现场反向传播训练全机械神经网络进行任务学习