Xu Jingxi, Chen Ava, Winterbottom Lauren, Palacios Joaquin, Chivukula Preethika, Nilsen Dawn M, Stein Joel, Ciocarlie Matei
IEEE Int Conf Rehabil Robot. 2025 May;2025:1512-1517. doi: 10.1109/ICORR66766.2025.11062977.
Intent inferral, the process by which a robotic device predicts a user's intent from biosignals, offers an effective and intuitive way to control wearable robots. Classical intent inferral methods treat biosignal inputs as unidirectional ground truths for training machine learning models, where the internal state of the model is not directly observable by the user. In this work, we propose reciprocal learning, a bidirectional paradigm that facilitates human adaptation to an intent inferral classifier. Our paradigm consists of iterative, interwoven stages that alternate between updating machine learning models and guiding human adaptation with the use of augmented visual feedback. We demonstrate this paradigm in the context of controlling a robotic hand orthosis for stroke, where the device predicts open, close, and relax intents from electromyographic (EMG) signals and provides appropriate assistance. We use LED progress-bar displays to communicate to the user the predicted probabilities for open and close intents by the classifier. Our experiments with stroke subjects show reciprocal learning improving performance in a subset of subjects (two out of five) without negatively impacting performance on the others. We hypothesize that, during reciprocal learning, subjects can learn to reproduce more distinguishable muscle activation patterns and generate more separable biosignals.
意图推断是一种机器人设备从生物信号中预测用户意图的过程，它为控制可穿戴机器人提供了一种有效且直观的方式。传统的意图推断方法将生物信号输入视为训练机器学习模型的单向基本事实，而模型的内部状态用户无法直接观察到。在这项工作中，我们提出了交互学习，这是一种双向范式，有助于人类适应意图推断分类器。我们的范式由迭代、交织的阶段组成，这些阶段在更新机器学习模型和利用增强视觉反馈引导人类适应之间交替进行。我们在控制用于中风患者的机器人手部矫形器的背景下展示了这种范式，该设备从肌电图（EMG）信号中预测打开、关闭和放松意图，并提供适当的辅助。我们使用LED进度条显示器向用户传达分类器对打开和关闭意图的预测概率。我们对中风患者进行的实验表明，交互学习在一部分受试者（五分之二）中提高了性能，同时对其他受试者的性能没有负面影响。我们推测，在交互学习过程中，受试者可以学会重现更具区分性的肌肉激活模式，并生成更可分离的生物信号。
