  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.
