COVID-19 may drive sustained research in robotics to address risks of infectious diseases.

Based on concepts of the human visual system, computational visual attention systems aim to detect regions of interest in images. Psychologists, neurobiologists, and computer scientists have investigated visual attention thoroughly during the last decades and profited considerably from each other. However, the interdisciplinarity of the topic holds not only benefits but also difficulties: Concepts of other fields are usually hard to access due to differences in vocabulary and lack of knowledge of the relevant literature. This article aims to bridge this gap and bring together concepts and ideas from the different research areas. It provides an extensive survey of the grounding psychological and biological research on visual attention as well as the current state of the art of computational systems. Furthermore, it presents a broad range of applications of computational attention systems in fields like computer vision, cognitive systems, and mobile robotics. We conclude with a discussion on the limitations and open questions in the field.

Robots have entered our domestic lives, but yet, little is known about their impact on the home. This paper takes steps towards addressing this omission, by reporting results from an empirical study of iRobot's Roomba™, a vacuuming robot. Our findings suggest that, by developing intimacy to the robot, our participants were able to derive increased pleasure from cleaning, and expended effort to fit Roomba into their homes, and shared it with others. These findings lead us to propose four design implications that we argue could increase people's enthusiasm for smart home technologies.

We consider the following problem: a team of robots is deployed in an unknown environment and it has to collaboratively build a map of the area without a reliable infrastructure for communication. The backbone for modern mapping techniques is pose graph optimization, which estimates the trajectory of the robots, from which the map can be easily built. The first contribution of this paper is a set of distributed algorithms for pose graph optimization: rather than sending all sensor data to a remote sensor fusion server, the robots exchange very partial and noisy information to reach an agreement on the pose graph configuration. Our approach can be considered as a distributed implementation of the two-stage approach of Carlone et al. (2015b), where we use the Successive Over-Relaxation (SOR) and the Jacobi Over-Relaxation (JOR) as workhorses to split the computation among the robots. We also provide conditions under which the proposed distributed protocols converge to the solution of the centralized two-stage approach. As a second contribution, we extend the proposed distributed algorithms to work with object-based map models. The use of objectbased models avoids the exchange of raw sensor measurements (e.g., point clouds or RGB-D data) further reducing the communication burden. Our third contribution is an extensive experimental evaluation of the proposed techniques, including tests in realistic Gazebo simulations and field experiments in a military test facility. Abundant experimental evidence suggests that one of the proposed algorithms (the Distributed Gauss-Seidel method or DGS) has excellent performance. The DGS requires minimal information exchange, has an anytime flavor, scales well to large teams (we demonstrate mapping with a team of 50 robots), is robust to noise, and is easy to implement. Our field tests show that the combined use of our distributed algorithms and object-based models reduces the communication requirements by several orders of magnitude and enables distributed mapping with large teams of robots in real-world problems.

In this paper we describe the problem of Visual Place Categorization (VPC) for mobile robotics, which involves predicting the semantic category of a place from image measurements acquired from an autonomous platform. For example, a robot in an unfamiliar home environment should be able to recognize the functionality of the rooms it visits, such as kitchen, living room, etc. We describe an approach to VPC based on sequential processing of images acquired with a conventional video camera. We identify two key challenges: Dealing with non-characteristic views and integrating restricted-FOV imagery into a holistic prediction. We present a solution to VPC based upon a recently-developed visual feature known as CENTRIST (CENsus TRansform hISTogram). We describe a new dataset for VPC which we have recently collected and are making publicly available. We believe this is the first significant, realistic dataset for the VPC problem. It contains the interiors of six different homes with ground truth labels. We use this dataset to validate our solution approach, achieving promising results.• The use of vision as the main sensing modality. As cameras are becoming cheaper and on-board computers

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It has been recognized that long-term effects exist in the interaction with robotic technologies. Despite this recognition, we still know little about how the temporal effects are associated with domestic robots. To bridge this gap, we undertook a long-term field study. We distributed Roomba vacuuming robots to 30 households, and observed the use over six months. During this study, which spans over 149 home visits, we identified how householders accepted robots as a part of the households via four temporal stages of pre-adoption, adoption, adaptation, and use/retention. With these findings, we took the first step toward establishing a framework, Domestic Robot Ecology (DRE). It shows a holistic view on the relationships that robots shape in the home. Further, it articulates how those relationships change over time. We suggest that DRE can become a useful tool to help design toward long-term acceptance of robotic technologies in the home.

Comparative analyses of avian population fluctuations have shown large interspecific differences in population variability that have been difficult to relate to variation in general ecological characteristics. Here we show that interspecific variation in demographic stochasticity, caused by random variation among individuals in their fitness contributions, can be predicted from a knowledge of the species' position along a "slow-fast" gradient of life-history variation, ranging from high reproductive species with short life expectancy at one end to species that often produce a single offspring but survive well at the other end of the continuum. The demographic stochasticity decreased with adult survival rate, age at maturity, and generation time or the position of the species toward the slow end of the slow-fast life-history gradient. This relationship between life-history characteristics and demographic stochasticity was related to interspecific differences in the variation among females in recruitment as well as to differences in the individual variation in survival. Because reproductive decisions in birds are often subject to strong natural selection, our results provide strong evidence for adaptive modifications of reproductive investment through life-history evolution of the influence of stochastic variation on avian population dynamics.