Averting Robot Eyes

Home robots will cause privacy harms. At the same time, they can provide beneficial services—as long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms. We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology

Comparing Features of Three-Dimensional Object Models Using Registration Based on Surface Curvature Signatures

This dissertation presents a technique for comparing local shape properties for similar three-dimensional objects represented by meshes. Our novel shape representation, the curvature map, describes shape as a function of surface curvature in the region around a point. A multi-pass approach is applied to the curvature map to detect features at different scales. The feature detection step does not require user input or parameter tuning. We use features ordered by strength, the similarity of pairs of features, and pruning based on geometric consistency to efficiently determine key corresponding locations on the objects. For genus zero objects, the corresponding locations are used to generate a consistent spherical parameterization that defines the point-to-point correspondence used for the final shape comparison

The Real Effect of Warm-Cool Colors

The phenomenon of warmer colors appearing nearer in depth to viewers than cooler colors has been studied extensively by psychologists and other vision researchers. The vast majority of these studies have asked human observers to view physically equidistant, colored stimuli and compare them for relative depth. However, in most cases, the stimuli presented were rather simple: straight colored lines, uniform color patches, point light sources, or symmetrical objects with uniform shading. Additionally, the colors used were typically highly saturated. Although such stimuli are useful in isolating and studying depth cues in certain contexts, they leave open the question of whether the human visual system operates similarly for realistic objects. This paper presents the results of an experiment designed to explore the color-depth relationship for realistic, colored objects with varying shading and contour

Picture Composition for a Robot Photographer

We explain how to use simple composition rules to drive an automated, mobile photography system. The composition rules are used to determine both the location for a good photograph, and how to frame that photograph. We describe the composition component in the context of a larger application, a robotic photographer. The robot moves around an area with people in it, opportunistically looking for faces and taking photographs. We describe both how to find faces in the world and how to create “good” photographs of those faces

Averting Robot Eyes

Home robots will cause privacy harms. At the same time, they can provide beneficial services—as long as consumers trust them. This Essay evaluates potential technological solutions that could help home robots keep their promises, avert their eyes, and otherwise mitigate privacy harms. Our goals are to inform regulators of robot-related privacy harms and the available technological tools for mitigating them, and to spur technologists to employ existing tools and develop new ones by articulating principles for avoiding privacy harms. We posit that home robots will raise privacy problems of three basic types: (1) data privacy problems; (2) boundary management problems; and (3) social/relational problems. Technological design can ward off, if not fully prevent, a number of these harms. We propose five principles for home robots and privacy design: data minimization, purpose specifications, use limitations, honest anthropomorphism, and dynamic feedback and participation. We review current research into privacy-sensitive robotics, evaluating what technological solutions are feasible and where the harder problems lie. We close by contemplating legal frameworks that might encourage the implementation of such design, while also recognizing the potential costs of regulation at these early stages of the technology

Using Texture Synthesis for Non-Photorealistic Shading from Paint Samples

This paper presents several methods for shading meshes from scanned paint samples that represent dark to light transitions. Our techniques emphasize artistic control of brush stroke texture and color. We first demonstrate how the texture of the paint sample can be separated from its color gradient. We demonstrate three methods, two real-time and one off-line for producing rendered, shaded images from the texture samples. All three techniques use texture synthesis to generate additional paint samples. Finally, we develop metrics for evaluating how well each method achieves our goal in terms of texture similarity, shading correctness and temporal coherence

An Education Theory of Fault For Autonomous Systems

Automated systems like self-driving cars and “smart” thermostats are a challenge for fault-based legal regimes like negligence because they have the potential to behave in unpredictable ways. How can people who build and deploy complex automated systems be said to be at fault when they could not have reasonably anticipated the behavior (and thus risk) of their tools?Part of the problem is that the legal system has yet to settle on the language for identifying culpable behavior in the design and deployment for automated systems. In this article we offer an education theory of fault for autonomous systems—a new way to think about fault for all the relevant stakeholders who create and deploy “smart” technologies. We argue that the most important failures that lead autonomous systems to cause unpredictable harm are due to the lack of communication, clarity, and education between the procurer, developer, and users of these technologies.In other words, while it is hard to exert meaningful control over automated systems to get them to act predictably, developers and procurers have great control over how much they test these tools and articulate their limits to all the other relevant parties. This makes testing and education one of the most legally relevant point of failures when automated systems harm people. By recognizing a responsibility to test and educate each other, foreseeable errors can be reduced, more accurate expectations can be set, and autonomous systems can be made more predictable and safer