用自监督学习让机器学会触摸软体，实现类人触诊。
Toward Artificial Palpation: Representation Learning of Touch on Soft Bodies
- 通过编码器-解码器架构从触觉序列中学习物体表征。
- 在仿真与真实数据上验证了触觉成像和变化检测效果。
- 适合做医疗触觉感知、机器人触觉建模的研究者。
触诊是医学检查中几乎完全依赖人工的触觉操作。本文提出一种人工触诊的概念验证方法，基于自监督学习构建编码器-解码器框架，从一系列触觉测量中学习物体的表征，该表征包含对被测对象的全部相关信息。我们假设这种表征可用于下游任务如触觉成像与变化检测。通过足够的训练数据，模型可捕捉触觉信号中超越力图映射的复杂模式——当前技术的局限。为验证方法，我们构建了仿真环境并采集了真实世界软体样本及其对应的磁共振成像（MRI）真值图像。使用配备触觉传感器的机器人采集触诊序列，并训练模型预测物体不同位置的感官读数。研究学习到的表征，展示了其在触觉成像与变化检测中的应用。
原文摘要 · Abstract (English)
Palpation, the use of touch in medical examination, is almost exclusively performed by humans. We investigate a proof of concept for an artificial palpation method based on self-supervised learning. Our key idea is that an encoder-decoder framework can learn a $\textit{representation}$ from a sequence of tactile measurements that contains all the relevant information about the palpated object. We conjecture that such a representation can be used for downstream tasks such as tactile imaging and change detection. With enough training data, it should capture intricate patterns in the tactile measurements that go beyond a simple map of forces -- the current state of the art. To validate our approach, we both develop a simulation environment and collect a real-world dataset of soft objects and corresponding ground truth images obtained by magnetic resonance imaging (MRI). We collect palpation sequences using a robot equipped with a tactile sensor, and train a model that predicts sensory readings at different positions on the object. We investigate the representation learned in this process, and demonstrate its use in imaging and change detection.
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