With the rapid advancements in robotics, mobile service robots have become increasingly essential in assisting people with a wide variety of tasks, including domestic chores, healthcare, and package delivery [1, 2, 3]. To efficiently accomplish these tasks, most mobile robots are equipped with high-resolution cameras that capture detailed visual data of their operational environments. Although the usage of these visual sensors enhances robot performance, it simultaneously raises substantial privacy concerns [6], particularly when robots operate within users' personal or private spaces. Motivated by the critical need to balance robot performance and user privacy, it is important to understand user perceptions of privacy concerns related to robotic visual data collection. Previous studies have investigated privacy concerns related to general camera surveillance [4, 5], yet relatively few studies specifically focus on visual data modalities and image resolution for privacy in the context of mobile robotics. Therefore, we conducted a user study aimed at uncovering user preferences and attitudes regarding privacy risks associated with robotic visual perception. Specifically, our objectives include evaluating user opinions on privacy of different visual data modalities and determining user-preferred strategies and thresholds (e.g., reduced image resolution) for effective privacy preservation.

Kurt "The CyberGuy" Knutsson reveals the perks of the LawnMeister, an AI-powered lawn mower that can help you complete tedious yard work. When the Roomba was first released, I was ecstatic. The idea of having this cute little robot do a chore that I absolutely hated was such a treat. I even gave it a name so that whenever it missed a spot, I could yell at it to express my frustration. Luckily, we can soon add a member to the family tree, and I might have an opportunity to yell at yet another little robot who does my chores for me.

Keith M. Andress, coauthor of "Evidence Accumulation and Flow of Control in a Hierarchical Spatial Reasoning System, " is a research associate in the Robot Vision Lab at Purdue University His research interests are in formalisms for accumulation of evidence, expert systems, and computer vision. Steven J. Frank, author of "What AI Practitioners Should Know about the Law. Part Two" is an attorney practicing with Nutter, McClennen & Fish, One International Place, Boston, Massachusetts 02210-2699. Martin Herman, coauthor of "A Framework for Representing and Reasoning about Three-Dimensional Objects for Vision" is group leader of the Sensory Intelligence Group in the Robot Systems Division at the National Bureau of Standards, Gaithersburg, MD 20899. His research interests are robotics, robot vision, image understanding, world modeling, real-time planning, autonomous vehicles, and remotely operated vehicles Avinash C. Kak, coauthor of "Evidence Accumulation and Flow of Control in a Hierarchical Spatial Reasoning System, " is a professor of electrical engineering at Purdue University.

Except in fanciful movies like 2003's The Matrix Revolutions, where fearsome squid-like robots maneuvered with incredible ease, most robots are too clumsy to move around obstacles at high speeds. This is true in large part because they have trouble judging in the images they "see" just how far ahead obstacles are. This week, however, Stanford computer scientists will unveil a machine vision algorithm that gives robots the ability to approximate distances from single still images. "Many people have said that depth estimation from a single monocular image is impossible," says computer science Assistant Professor Andrew Ng, who will present a paper on his research at the Neural Information Processing Systems Conference in Vancouver Dec. 5-8. "I think this work shows that in practical problems, monocular depth estimation not only works well, but can also be very useful."

Sir James Dyson is taking on the might of Google by investing £5m in a British university to develop a new generation of "intelligent domestic robots". His company, best known for its vacuum cleaners, is putting the money into a laboratory at Imperial College London, which has begun hiring up to 15 scientists who will work on developing robot vision systems that could be used in devices such as robot-controlled vacuums – a longstanding ambition of Dyson himself. The inventor said the plan was to create "practical everyday technologies that will make our lives easier". The move could put Dyson into a position where it is directly challenging Google, which has recently acquired eight robotics companies, including Boston Dynamics, which has made self-controlling robots for the US military. In January it spent £400m acquiring DeepMind Technologies, a London-based startup focusing on artificial intelligence.

A major European study in which robots attempt to replicate human behavior related to vision, gripping objects, and spatial perception has been developed by researchers at Robotic Intelligence Laboratory of the Universitat Jaume I (UJI) in Spain. A robot head with moving eyes was integrated into a torso with articulated arms, built using computer models computer models from animal and human biology. The robot head uses an advanced 3-D visual system synchronized with robotic arms that allows robots to observe and be aware of their surroundings and also remember the contents of those images in order to act accordingly. The research design process included recording monkey neurons engaged in visual-motor coordination. Saccadic eye movement, related to the dynamic change of attention, was the first feature implemented in the vision system.