论文标题
POSEIT：视觉上的持有姿势数据集用于掌握稳定性分析
PoseIt: A Visual-Tactile Dataset of Holding Poses for Grasp Stability Analysis
论文作者
论文摘要
当人类掌握现实世界中的物体时，我们经常移动手臂将物体固定在不同的姿势中，我们可以使用它。相比之下，典型的实验室设置仅研究举起后立即研究抓握的稳定性，而无需任何随后重新放置手臂。但是，由于重力扭矩和握力接触力可能会完全改变，因此抓紧稳定性可能会根据对象的保持姿势而差异很大。为了促进对姿势如何影响抓紧稳定性的研究，我们提出了Poseit，这是一种新型的多模式数据集，其中包含从整个抓住对象的整个周期收集的视觉和触觉数据，将手臂重新放置到一个采样的姿势中，并摇动对象。使用Poseit的数据，我们可以制定和应对预测特定固定姿势是否稳定的握把对象的任务。我们培训一个LSTM分类器，该分类器在拟议的任务上达到85％的精度。我们的实验结果表明，接受Poseit训练的多模式模型比使用唯一视觉或触觉数据具有更高的精度，并且我们的分类器也可以推广到看不见的对象和姿势。
When humans grasp objects in the real world, we often move our arms to hold the object in a different pose where we can use it. In contrast, typical lab settings only study the stability of the grasp immediately after lifting, without any subsequent re-positioning of the arm. However, the grasp stability could vary widely based on the object's holding pose, as the gravitational torque and gripper contact forces could change completely. To facilitate the study of how holding poses affect grasp stability, we present PoseIt, a novel multi-modal dataset that contains visual and tactile data collected from a full cycle of grasping an object, re-positioning the arm to one of the sampled poses, and shaking the object. Using data from PoseIt, we can formulate and tackle the task of predicting whether a grasped object is stable in a particular held pose. We train an LSTM classifier that achieves 85% accuracy on the proposed task. Our experimental results show that multi-modal models trained on PoseIt achieve higher accuracy than using solely vision or tactile data and that our classifiers can also generalize to unseen objects and poses.
