用双向控制反馈机制提升机器人模仿学习的写字准确率
Error-Feedback Model for Output Correction in Bilateral Control-Based Imitation Learning
- 分层神经网络设计，下层通过反馈跟踪上层输出
- 在未训练字符书写任务中准确率显著提升
- 适合需要高精度动作修正的机器人控制场景
近年来，基于神经网络的模仿学习使机器人能够执行灵活任务。然而，由于神经网络采用前馈结构，缺乏对输出误差的补偿机制。为此，我们提出一种反馈机制：采用包含上下两层的分层神经网络结构，使下层受控于上层；同时在下层使用无内部状态的多层感知机，增强误差反馈能力。在汉字书写任务中，该模型在未训练字符上的书写准确率得到提升。通过带有误差反馈的自主控制，验证了下层能有效跟踪上层输出。本研究为神经网络与控制理论的融合提供了有前景的方向。
原文摘要 · Abstract (English)
In recent years, imitation learning using neural networks has enabled robots to perform flexible tasks. However, since neural networks operate in a feedforward structure, they do not possess a mechanism to compensate for output errors. To address this limitation, we developed a feedback mechanism to correct these errors. By employing a hierarchical structure for neural networks comprising lower and upper layers, the lower layer was controlled to follow the upper layer. Additionally, using a multi-layer perceptron in the lower layer, which lacks an internal state, enhanced the error feedback. In the character-writing task, this model demonstrated improved accuracy in writing previously untrained characters. In the character-writing task, this model demonstrated improved accuracy in writing previously untrained characters. Through autonomous control with error feedback, we confirmed that the lower layer could effectively track the output of the upper layer. This study represents a promising step toward integrating neural networks with control theories.
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