Abstract-We present a vision based multisensor that is designed for robot interaction with small, soft, and possibly fragile objects. The sensor consists of a rubber membrane, a rectangular frame on which the membrane is mounted and a CCD camera. The entire system is airtight. Based on the observed deformations of the membrane, we determine the contact area, the integral force acting on the membrane, the 3D force distribution over the membrane, and derive properties of the target object by monitoring the evolution of its deformation. We can distinguish between different types of materials, i.e., solid, soft, amorphous, and determine the speed and nature of their deformation. The sensitivity of the sensor can be adjusted by changing the volume of air within the rectangular frame. We achieved a small noise to signal ratio, which allows us to observe small integral forces in the range of 0.5 N to 2.5 N, with an average error of 0.04 N.

A novel method for quantitatively measuring social interactions on small temporal and spatial scales on the basis of interaction geometry (reduced to the parameters interpersonal distance and relative body orientation) with the help of infrared (IR) tracking is introduced. The method is intended to be used to establish a probabilistic classifier to identify existing social situations on the basis of measuring the two parameters for pairs of persons through a series of experiments. The classifier can then be used for characterizing the social context (as an evidence for or against established social situations) of users using sensors in mobile devices in view of useful future Mobile Social Networking services. A first experiment is conducted with the method, a number of standard classifiers including a Gaussian Mixture Model are trained and evaluated and the results are discussed.

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Lane change prediction of surrounding vehicles is a key building block of path planning. The focus has been on increasing the accuracy of prediction by posing it purely as a function estimation problem at the cost of model understandability. However, the efficacy of any lane change prediction model can be improved when both corner and failure cases are humanly understandable. We propose an attentionbased recurrent model to tackle both understandability and prediction quality. We also propose metrics which reflect the discomfort felt by the driver. We show encouraging results on a publicly available dataset and proprietary fleet data.