This paper presents Gambit, a custom, mid-cost 6-DoF robot manipulator system that can play physical board games against human opponents in non-idealized environments. Historically, unconstrained robotic manipulation in board games has often proven to be more challenging than the underlying game reasoning, making it an ideal testbed for small-scale manipulation. The Gambit system includes a low-cost Kinect-style visual sensor, a custom manipulator, and state-of-the-art learning algorithms for automatic detection and recognition of the board and objects on it. As a use-case, we describe playing chess quickly and accurately with arbitrary, uninstrumented boards and pieces, demonstrating that Gambit's engineering and design represent a new state-of-the-art in fast, robust tabletop manipulation.

We describe a robot that is able to autonomously plug itself in to standard, unmodified electrical outlets by sensing the 60Hz electric fields emitted from the outlet. The building electrical infrastructure is not modified in any way. Unlike previous powerline localization work, no additional signal is injected in the powerlines-the already present AC power carrier signal in the outlet is used as the localization beacon. This technique is faster, more accurate, and potentially less expensive than previously reported vision-based systems for autonomous plugging in.

Pretouch sensing is longer range than contact, but shorter range than vision. The hypothesis motivating this work is that closed loop feedback based on short range but non-contact measurements can improve the reliability of manipulation. This paper presents a grasping system that is guided at short range by Electric Field (EF) Pretouch. We describe two sets of experiments. The first set of experiments involves human-to-robot and robot-to-human handoff, including the use of EF Pretouch to detect whether or not a human is also touching an object that the robot is holding, which we call the “co-manipulation state.” In the second set of experiments, the robot picks up standalone objects. We describe a number of techniques that servo the arm and fingers in order to both collect relevant geometrical information, and to actually perform the manipulation task.

Increasingly, users access online services such as email, e-commerce, and social networking sites via 802.11-based wireless networks. As they do so, they expose a range of personal information such as their names, email addresses, and ZIP codes to anyone within broadcast range of the network. This paper presents results from an exploratory study that examined how users from the general public understand Wi-Fi, what their concerns are related to Wi-Fi use, and which practices they follow to counter perceived threats. Our results reveal that while users understand the practical details of Wi-Fi use reasonably well, they lack understanding of important privacy risks. In addition, users employ incomplete protective practices which results in a false sense of security and lack of concern while on Wi-Fi. Based on our results, we outline opportunities for technology to help address these problems.

Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.

Personal, mobile displays, such as those on mobile phones, are ubiquitous, yet for the most part, underutilized. We present results from a field experiment that investigated the effectiveness of these displays as a means for improving awareness of daily life (in our case, self-monitoring of physical activity). Twenty-eight participants in three experimental conditions used our UbiFit system for a period of three months in their day-to-day lives over the winter holiday season. Our results show, for example, that participants who had an awareness display were able to maintain their physical activity level (even during the holidays), while the level of physical activity for participants who did not have an awareness display dropped significantly. We discuss our results and their general implications for the use of everyday mobile devices as awareness displays.

Users’ ability to accurately recall frequent, habitual activities is fundamental to a number of disciplines, from health sciences to machine learning. However, few, if any, studies exist that have assessed optimal sampling strategies for in situ self-reports. In addition, few technologies exist that facilitate benchmarking self-report accuracy for routine activities. We report on a study investigating the effect of sampling frequency of self-reports of two routine activities (sitting and walking) on recall accuracy and annoyance. We used a novel wearable sensor platform that runs a real time activity inference engine to collect in situ ground truth. Our results suggest that a sampling frequency of five to eight times per day may yield an optimal balance of recall and annoyance. Additionally, requesting self-reports at regular, predetermined times increases accuracy while minimizing perceived annoyance since it allows participants to anticipate these requests. We discuss our results and their implications for future studies.