用仿人触觉探索策略提升机器人纹理识别精度与能效
Exploratory Movement Strategies for Texture Discrimination with a Neuromorphic Tactile Sensor
- 模拟人类触觉探索，用滑动+旋转动作采集神经形态触觉数据
- 在复杂条件下实现87.33%准确率，功耗仅8.04mW
- 适合低功耗智能机器人触觉交互场景
我们提出一种受人类探索策略启发的神经形态触觉感知框架，用于机器人纹理分类。系统利用NeuroTac传感器在一系列探索运动中采集神经形态触觉数据。首先在固定环境下测试了六种运动：滑动、旋转、敲击，以及组合运动：滑动+旋转、敲击+旋转、敲击+滑动。基于最终准确率和达到收敛所需采样时长，选定滑动和滑动+旋转为最优运动。第二阶段实验模拟真实复杂环境，在不同接触深度与速度下评估这两种运动。结果显示，滑动+旋转策略在该条件下取得最高87.33%准确率，同时保持仅8.04mW的极低功耗。结果表明，滑动+旋转是神经形态触觉感知在纹理分类任务中的最优探索策略，对提升机器人环境交互能力具有重要前景。
原文摘要 · Abstract (English)
We propose a neuromorphic tactile sensing framework for robotic texture classification that is inspired by human exploratory strategies. Our system utilizes the NeuroTac sensor to capture neuromorphic tactile data during a series of exploratory motions. We first tested six distinct motions for texture classification under fixed environment: sliding, rotating, tapping, as well as the combined motions: sliding+rotating, tapping+rotating, and tapping+sliding. We chose sliding and sliding+rotating as the best motions based on final accuracy and the sample timing length needed to reach converged accuracy. In the second experiment designed to simulate complex real-world conditions, these two motions were further evaluated under varying contact depth and speeds. Under these conditions, our framework attained the highest accuracy of 87.33\% with sliding+rotating while maintaining an extremely low power consumption of only 8.04 mW. These results suggest that the sliding+rotating motion is the optimal exploratory strategy for neuromorphic tactile sensing deployment in texture classification tasks and holds significant promise for enhancing robotic environmental interaction.
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