This paper introduces a novel interactive approach —Exploratory Experiences— that aims to improve the ability of people to reason about the capabilities and limitations of robotic technology. We focus on two areas: robot navigation and object detection. We evaluate the Exploratory Experiences with a novel approach that measures the participant's ability to predict when the robot will fail, following up with asking the reason and a possible fix. We show that our approach is effective at improving participants' understanding of potential robot navigation failures and that they already have the skills to detect potential object detection failures when presented with the correct stimuli.

This paper explores whether experiential knowledge of computer vision from interacting with daily apps (e.g., Instagram, Zoom, etc.) can be leveraged to improve users’ expectations of robotic capabilities. We evaluate users’ ability to predict when computer vision apps might fail and if they can apply their experience to reason about computer vision in robotic systems. We show that although users can reliably predict computer vision app capabilities and functionality, they tend to ascribe human-level knowledge to those apps and do not reliably correlate app functionality with similar robotic tasks. We propose that experiential knowledge gained through interaction with software apps is a potential way to “calibrate” user expectations of the function and failure states of complex systems.

In this paper we investigate the influence interfaces and feedback have on human-robot trust levels when operating in a shared physical space. The task we use is specifying a “no-go” region for a robot in an indoor environment. We evaluate three styles of interface (physical, AR, and map-based) and four feedback mechanisms (no feedback, robot drives around the space, an AR “fence”, and the region marked on the map). Our evaluation looks at both usability and trust. Specifically, if the participant trusts that the robot “knows” where the no-go region is and their confidence in the robot's ability to avoid that region. We use both self-reported and indirect measures of trust and usability. Our key findings are: 1) interfaces and feedback do influence levels of trust; 2) the participants largely preferred a mixed interface-feedback pair, where the modality for the interface differed from the feedback.

Camera keyframing is an integral part of the animation making process. The animator places the camera in a sequence of "key" positions, and the computer produces a set of intermediate camera locations that interpolates between these keyframes. Camera keyframing traditionally treats the camera as just another 3D object in the scene, with intermediate frames produced by interpolating the position and focal point of the camera in space. Unlike a 3D object, however, the camera's role is to project the 3D scene into 2D. The animator indirectly controls the projection --- how objects in the scene are placed in the 2D image --- by adjusting the camera parameters for each keyframe. Automatic 3D camera interpolation adds yet another layer of indirection. The net effect is that the animator must solve a complicated inverse problem in order to move objects across the 2D scene in the desired manner.

Shepherding behaviors are a type of flocking behavior in which outside agents guide or control members of a flock. Shepherding behaviors can be found in various forms in nature. For example, herding, covering, patrolling and collecting are common types of shepherding behaviors. In this work, we investigate ways to simulate these types of behaviors. A shepherd uses roadmaps to steer the flock and to re-group separated flock members. This paper focuses on improving the shepherd's movements to gain better control of the flock's motion and use this improved control to demonstrate a wider variety of shepherding behaviors.