Understanding Learned Models by Identifying Important Features at the Right Resolution

In many application domains, it is important to characterize how complex learned models make their decisions across the distribution of instances. One way to do this is to identify the features and interactions among them that contribute to a model's predictive accuracy. We present a model-agnostic approach to this task that makes the following specific contributions. Our approach (i) tests feature groups, in addition to base features, and tries to determine the level of resolution at which important features can be determined, (ii) uses hypothesis testing to rigorously assess the effect of each feature on the model's loss, (iii) employs a hierarchical approach to control the false discovery rate when testing feature groups and individual base features for importance, and (iv) uses hypothesis testing to identify important interactions among features and feature groups. We evaluate our approach by analyzing random forest and LSTM neural network models learned in two challenging biomedical applications.

Pneumatic Shape-shifting Fingers to Reorient and Grasp

We present pneumatic shape-shifting fingers to enable a simple parallel-jaw gripper for different manipulation modalities. By changing the finger geometry, the gripper effectively changes the contact type between the fingers and an object to facilitate distinct manipulation primitives. In this paper, we demonstrate the development and application of shape-shifting fingers to reorient and grasp cylindrical objects. The shape of the fingers changes based on the air pressure inside them and attains two distinct geometric forms at high and low pressure values. In our implementation, the finger shape switches between a wedge-shaped geometry and V-shaped geometry at high and low pressure, respectively. Using the wedge-shaped geometry, the fingers provide a point contact on a cylindrical object to pivot it to a vertical pose under the effect of gravity. By changing to V-shaped geometry, the fingers localize the object in the vertical pose and securely hold it. Experimental results show that the smooth transition between the two contact types allows a robot with a simple gripper to reorient a cylindrical object lying horizontally on a ground and to grasp it in a vertical pose.