用仿生肌梭编码触觉信号，实现软体手对物体的快速分类。
Object Classification Utilizing Neuromorphic Proprioceptive Signals in Active Exploration: Validated on a Soft Anthropomorphic Hand
- 通过生物肌梭模型编码软体手的本体感觉信号。
- 在探索初期即实现比现有方法更准确的物体分类。
- 适合神经假肢和触觉反馈系统研究者参考。
本体感觉是触觉感知中感知物体三维结构的关键感官模态，通过提供身体部位位置与运动的反馈发挥作用。恢复本体感觉对实现假肢手的手中操作与自然控制至关重要。尽管其重要性显著，人工系统中对本体感觉的研究仍相对匮乏。本文提出一个新平台，将软体仿人机械手（QB SoftHand）与柔性本体感觉传感器结合，采用混合脉冲神经网络（含多种脉冲神经元）解析由生物肌梭模型编码的神经形态本体感觉信号。该编码方案与分类器在主动探索YCB基准中10个物体的数据集上进行了测试。结果表明，该分类器在探索初期的推断准确性优于现有学习方法。该系统为触觉反馈与神经假肢领域的发展提供了可能。
原文摘要 · Abstract (English)
Proprioception, a key sensory modality in haptic perception, plays a vital role in perceiving the 3D structure of objects by providing feedback on the position and movement of body parts. The restoration of proprioceptive sensation is crucial for enabling in-hand manipulation and natural control in the prosthetic hand. Despite its importance, proprioceptive sensation is relatively unexplored in an artificial system. In this work, we introduce a novel platform that integrates a soft anthropomorphic robot hand (QB SoftHand) with flexible proprioceptive sensors and a classifier that utilizes a hybrid spiking neural network with different types of spiking neurons to interpret neuromorphic proprioceptive signals encoded by a biological muscle spindle model. The encoding scheme and the classifier are implemented and tested on the datasets we collected in the active exploration of ten objects from the YCB benchmark. Our results indicate that the classifier achieves more accurate inferences than existing learning approaches, especially in the early stage of the exploration. This system holds the potential for development in the areas of haptic feedback and neural prosthetics.
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