We present Neural Contact Fields, a method that brings together neural fields and tactile sensing to address the problem of tracking extrinsic contact between object and environment. Knowing where the external contact occurs is a first step towards methods that can actively control it in facilitating downstream manipulation tasks. Prior work for localizing environmental contacts typically assume a contact type (e.g. point or line), does not capture contact/no-contact transitions, and only works with basic geometric-shaped objects. Neural Contact Fields are the first method that can track arbitrary multi-modal extrinsic contacts without making any assumptions about the contact type. Our key insight is to estimate the probability of contact for any 3D point in the latent space of object shapes, given vision-based tactile inputs that sense the local motion resulting from the external contact. In experiments, we find that Neural Contact Fields are able to localize multiple contact patches without making any assumptions about the geometry of the contact, and capture contact/no-contact transitions for known categories of objects with unseen shapes in unseen environment configurations. In addition to Neural Contact Fields, we also release our YCB-Extrinsic-Contact dataset of simulated extrinsic contact interactions to enable further research in this area. Project page: https://github.com/carolinahiguera/NCF
翻译:我们介绍神经接触场,这是将神经字段和触觉感测汇集在一起的方法,用来解决跟踪物体和环境之间外部接触的问题。知道外部接触发生在何处是朝着采取能够积极控制它的方法的第一步,以便有助于下游操作任务。环境接触的先前定位工作通常采取一种接触类型(例如点或线),不捕捉接触/无接触过渡,并且只与基本的几何形物体合作。神经接触场是第一个可以追踪任意的多模式接触方式,而不对接触类型作出任何假设的方法。我们的关键洞察力是估计物体形状潜在空间中任何3D点的接触概率,基于视觉的触觉投入可以感知外部接触产生的当地运动。在实验中,我们发现神经接触场能够在不对接触的几何形状作任何假设的情况下将多个接触点本地化。此外,除了神经接触外观外观外观外观外观外观外观外观,我们还可以在视觉环境配置的已知形状对象类别中进行接触/接触/接触/接触。此外,我们还可以在内部互动领域进行模拟的外部接触。</s>