Humans have an exquisite sense of touch which robotic and prosthetic systems aim to recreate. We developed algorithms to create neuron-like (neuromorphic) spiking representations of texture that are invariant to the scanning speed and contact force applied in the sensing process. The spiking representations are based on mimicking activity from mechanoreceptors in human skin and further processing up to the brain. The neuromorphic encoding process transforms analog sensor readings into speed and force invariant spiking representations in three sequential stages: the force invariance module (in the analog domain), the spiking activity encoding module (transforms from analog to spiking domain), and the speed invariance module (in the spiking domain). The algorithms were tested on a tactile texture dataset collected in 15 speed-force conditions. An offline texture classification system built on the invariant representations has higher classification accuracy, improved computational efficiency, and increased capability to identify textures explored in novel speed-force conditions. The speed invariance algorithm was adapted to a real-time human-operated texture classification system. Similarly, the invariant representations improved classification accuracy, computational efficiency, and capability to identify textures explored in novel conditions. The invariant representation is even more crucial in this context due to human imprecision which seems to the classification system as a novel condition. These results demonstrate that invariant neuromorphic representations enable better performing neurorobotic tactile sensing systems. Furthermore, because the neuromorphic representations are based on biological processing, this work can be used in the future as the basis for naturalistic sensory feedback for upper limb amputees.

翻译：人类拥有精妙的触觉感知能力，而机器人及假肢系统正致力于复现这种能力。我们开发了算法以生成具有神经元样（神经形态）脉冲发放特性的纹理表征，该表征对感知过程中的扫描速度与接触力具有不变性。该脉冲表征基于模拟人体皮肤中机械感受器的活动及其向大脑传递的进一步处理过程。神经形态编码过程通过三个连续阶段将模拟传感器读数转换为具有速度与力不变性的脉冲表征：力不变性模块（模拟域）、脉冲活动编码模块（从模拟域转换至脉冲域）以及速度不变性模块（脉冲域）。算法在15种速度-力条件下采集的触觉纹理数据集上进行了测试。基于不变性表征构建的离线纹理分类系统展现出更高的分类准确率、更优的计算效率，以及识别在新速度-力条件下探索的纹理的增强能力。速度不变性算法被应用于实时人机交互纹理分类系统。同样地，不变性表征提升了分类准确率、计算效率及对新条件下纹理的识别能力。在此场景中，不变性表征尤为重要，因为人类操作的不精确性对分类系统而言等同于新条件。这些结果表明，不变性神经形态表征能够构建性能更优的神经机器人触觉感知系统。此外，由于该神经形态表征基于生物处理机制，本工作未来可作为上肢截肢者自然感觉反馈的基础。