Prior work in human-autonomy interaction has focused on plant systems that operate in highly structured environments. In contrast, many human-robot interaction (HRI) tasks are dynamic and unstructured, occurring in the open world. It is our belief that methods developed for the measurement and modeling of trust in traditional automation need alteration in order to be useful for HRI. Therefore, it is important to characterize the factors in HRI that influence trust. This study focused on the influence of changing autonomy reliability. Participants experienced a set of challenging robot handling scenarios that forced autonomy use and kept them focused on autonomy performance. The counterbalanced experiment included scenarios with different low reliability windows so that we could examine how drops in reliability altered trust and use of autonomy. Drops in reliability were shown to affect trust, the frequency and timing of autonomy mode switching, as well as participants' self-assessments of performance. A regression analysis on a number of robot, personal, and scenario factors revealed that participants tie trust more strongly to their own actions rather than robot performance.

We present semi-supervised deep learning approaches for traversability estimation from fisheye images. Our method, GONet, and the proposed extensions leverage Generative Adversarial Networks (GANs) to effectively predict whether the area seen in the input image(s) is safe for a robot to traverse. These methods are trained with many positive images of traversable places, but just a small set of negative images depicting blocked and unsafe areas. This makes the proposed methods practical. Positive examples can be collected easily by simply operating a robot through traversable spaces, while obtaining negative examples is time consuming, costly, and potentially dangerous. Through extensive experiments and several demonstrations, we show that the proposed traversability estimation approaches are robust and can generalize to unseen scenarios. Further, we demonstrate that our methods are memory efficient and fast, allowing for real-time operation on a mobile robot with single or stereo fisheye cameras. As part of our contributions, we open-source two new datasets for traversability estimation. These datasets are composed of approximately 24h of videos from more than 25 indoor environments. Our methods outperform baseline approaches for traversability estimation on these new datasets.

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A B C Figure 1. 3D printed pneumatic button (A), knob (B), and slider (C). We print these models with rigid and elastic materials, and in some cases add electronic components to the controls to sense user's input, such as potentiometers in the case of (B) and (C). Pressure sensing can be used to detect when users deform the elastic parts of the controls, like when they push the star. The graph behind this model shows the sensed pressure signal. We also use pressure to provide variable activation force capabilities. ABSTRACTWe explore 3D printing physical controls whose tactile response can be manipulated programmatically through pneumatic actuation. In particular, by manipulating the internal air pressure of various pneumatic elements, we can create mechanisms that require different levels of actuation force and can also change their shape. We introduce and discuss a series of example 3D printed pneumatic controls, which demonstrate the feasibility of our approach. This includes conventional controls, such as buttons, knobs and sliders, but also extends to domains such as toys and deformable interfaces. We describe the challenges that we faced and the methods that we used to overcome some of the limitations of current 3D printing technology. We conclude with example applications and thoughts on future avenues of research.