论文标题
tac2pose:触觉对象姿势构成第一触点的估计
Tac2Pose: Tactile Object Pose Estimation from the First Touch
论文作者
论文摘要
在本文中,我们介绍了TAC2POSE,这是一种特定于对象的触觉方法,从首次触摸已知对象构成构成估计。鉴于对象几何形状,我们在模拟中学习了一个量身定制的感知模型,该模型估计了给定触觉观察的可能对象姿势的概率分布。为此,我们模拟了一个密集的物体姿势将在传感器上产生的密集对象姿势的接触形状。然后,鉴于从传感器获得的新接触形状,我们使用使用对比度学习学习的对象特定的嵌入方式将其与预计的集合进行匹配。我们从传感器中获得了接触形状,并具有对象不足的校准步骤,该步骤将RGB触觉观测值映射到二进制接触形状。该映射可以在对象和传感器实例上重复使用,是唯一训练有真实传感器数据的步骤。这导致了一种感知模型,该模型将从第一个真实触觉观察中定位对象。重要的是,它产生姿势分布,并可以纳入来自其他感知系统,联系人或先验的其他姿势限制。 我们为20个对象提供定量结果。 TAC2POSE从独特的触觉观测中提供了高精度的姿势估计,同时回归有意义的姿势分布,以说明可能由不同对象姿势产生的接触形状。我们还测试了从3D扫描仪重建的对象模型上的TAC2POSE,以评估对象模型中不确定性的鲁棒性。最后,我们证明了TAC2POSE的优势与三种基线方法进行触觉姿势估计:直接使用神经网络将物体姿势回归,使用标准分类神经网络将观察到的接触与一组可能的触点匹配,并直接将观察到的接触的像素比较与一组可能的接触。 网站:http://mcube.mit.edu/research/tac2pose.html
In this paper, we present Tac2Pose, an object-specific approach to tactile pose estimation from the first touch for known objects. Given the object geometry, we learn a tailored perception model in simulation that estimates a probability distribution over possible object poses given a tactile observation. To do so, we simulate the contact shapes that a dense set of object poses would produce on the sensor. Then, given a new contact shape obtained from the sensor, we match it against the pre-computed set using an object-specific embedding learned using contrastive learning. We obtain contact shapes from the sensor with an object-agnostic calibration step that maps RGB tactile observations to binary contact shapes. This mapping, which can be reused across object and sensor instances, is the only step trained with real sensor data. This results in a perception model that localizes objects from the first real tactile observation. Importantly, it produces pose distributions and can incorporate additional pose constraints coming from other perception systems, contacts, or priors. We provide quantitative results for 20 objects. Tac2Pose provides high accuracy pose estimations from distinctive tactile observations while regressing meaningful pose distributions to account for those contact shapes that could result from different object poses. We also test Tac2Pose on object models reconstructed from a 3D scanner, to evaluate the robustness to uncertainty in the object model. Finally, we demonstrate the advantages of Tac2Pose compared with three baseline methods for tactile pose estimation: directly regressing the object pose with a neural network, matching an observed contact to a set of possible contacts using a standard classification neural network, and direct pixel comparison of an observed contact with a set of possible contacts. Website: http://mcube.mit.edu/research/tac2pose.html