  In-flight objects capture is extremely challenging. The robot is required to
complete trajectory prediction, interception position calculation and motion
planning in sequence within tens of milliseconds. As in-flight uneven objects
are affected by various kinds of forces, motion prediction is difficult for a
time-varying acceleration. In order to compensate the system's non-linearity,
we introduce the Neural Acceleration Estimator (NAE) that estimates the varying
acceleration by observing a small fragment of previous deflected trajectory.
Moreover, end-to-end training with Differantiable Filter (NAE-DF) gives a
supervision for measurement uncertainty and further improves the prediction
accuracy. Experimental results show that motion prediction with NAE and NAE-DF
is superior to other methods and has a good generalization performance on
unseen objects. We test our methods on a robot, performing velocity control in
real world and respectively achieve 83.3% and 86.7% success rate on a ploy
urethane banana and a gourd. We also release an object in-flight dataset
containing 1,500 trajectorys for uneven objects.
