We describe an autonomous robotic system capable of navigating through an office environment, opening doors along the way, and plugging itself into electrical outlets to recharge as needed. We demonstrate through extensive experimentation that our robot executes these tasks reliably, without requiring any modification to the environment. We present robust detection algorithms for doors, door handles, and electrical plugs and sockets, combining vision and laser sensors. We show how to overcome the unavoidable shortcoming of perception by integrating compliant control into manipulation motions. We present a visual-differencing approach to high-precision plug-insertion that avoids the need for high-precision hand-eye calibration.

Prominent feature point descriptors such as SIFT and SURF allow reliable real-time matching but at a computational cost that limits the number of points that can be handled on PCs, and even more on less powerful mobile devices. A recently proposed technique that relies on statistical classification to compute signatures has the potential to be much faster but at the cost of using very large amounts of memory, which makes it impractical for implementation on low-memory devices. In this paper, we show that we can exploit the sparseness of these signatures to compact them, speed up the computation, and drastically reduce memory usage. We base our approach on Compressive Sensing theory. We also highlight its effectiveness by incorporating it into two very different SLAM packages and demonstrating substantial performance increases.

As our business, academic, and personal lives continue to move at an ever-faster pace, finding times for busy people to meet has become an art. One of the most perplexing challenges facing groupware is effective asynchronous group scheduling (GS). This paper presents a lightweight interaction model for GS that can extend its reach beyond users of current group calendaring solutions. By expressing availability in terms of preferences, we create a flexible framework for GS that preserves plausible deniability while exerting social pressure to encourage honesty among users. We also propose an ontology that enables us to model user preferences with machine learning, predicting user responses to further lower cognitive load. The combination of visualization/direct manipulation with machine learning allows users to easily and efficiently optimize meeting times. We also suggest resulting design implications for this class of intelligent user interfaces.