Humans display the remarkable ability to sense the world through tools and other held objects. For example, we are able to pinpoint impact locations on a held rod and tell apart different textures using a rigid probe. In this work, we consider how we can enable robots to have a similar capacity, i.e., to embody tools and extend perception using standard grasped objects. We propose that vibro-tactile sensing using dynamic tactile sensors on the robot fingers, along with machine learning models, enables robots to decipher contact information that is transmitted as vibrations along rigid objects. This paper reports on extensive experiments using the BioTac micro-vibration sensor and a new event dynamic sensor, the NUSkin, capable of multi-taxel sensing at 4~kHz. We demonstrate that fine localization on a held rod is possible using our approach (with errors less than 1 cm on a 20 cm rod). Next, we show that vibro-tactile perception can lead to reasonable grasp stability prediction during object handover, and accurate food identification using a standard fork. We find that multi-taxel vibro-tactile sensing at sufficiently high sampling rate led to the best performance across the various tasks and objects. Taken together, our results provides both evidence and guidelines for using vibro-tactile perception to extend tactile perception, which we believe will lead to enhanced competency with tools and better physical human-robot-interaction.
翻译:人类展示了通过工具和其他持有的物体来感知世界的非凡能力。 例如, 我们能够用僵硬的探测器来定位被搁置的棒子的撞击位置, 并用一个硬质探测器来分辨不同的纹理。 在这项工作中, 我们考虑如何让机器人拥有类似的能力, 即: 使用标准被捕获的物体来体现工具和扩展感知。 我们建议使用机器人手指上的动态触动感应器以及机器学习模型进行振动感应, 使机器人能够解析作为振动在僵硬的物体上传送的接触信息。 本文报告了使用BioTac微振动传感器和一个新的事件动态传感器( NUSkin, 能在4~kHz 上进行多压感知的多压感应器)进行的广泛实验。 我们证明, 使用我们的方法( 20厘米杆上的误差小于1厘米 ), 我们显示振动触觉感应能够导致在物体交接过程中合理掌握稳定性预测, 以及使用标准叉子精确的食品识别。 我们发现, 多税振动微振动感测, 将使用高感应力感测, 和高感测, 提供超高感应 。