Since its official introduction in 2012, the Robot Web Tools project has grown tremendously as an open-source community, enabling new levels of interoperability and portability across heterogeneous robot systems, devices, and front-end user interfaces. At the heart of Robot Web Tools is the rosbridge protocol as a general means for messaging ROS topics in a client-server paradigm suitable for wide area networks, and human-robot interaction at a global scale through modern web browsers. Building from rosbridge, this paper describes our efforts with Robot Web Tools to advance: 1) human-robot interaction through usable client and visualization libraries for more efficient development of front-end human-robot interfaces, and 2) cloud robotics through more efficient methods of transporting high-bandwidth topics (e.g., kinematic transforms, image streams, and point clouds). We further discuss the significant impact of Robot Web Tools through a diverse set of use cases that showcase the importance of a generic messaging protocol and front-end development systems for human-robot interaction.

Many navigation systems, including the ubiquitous ROS navigation stack, perform path-planning on a single costmap, in which the majority of information is stored in a single grid. This approach is quite successful at generating collision-free paths of minimal length, but it can struggle in dynamic, people-filled environments when the values in the costmap expand beyond occupied or free space. We have created and implemented a new method called layered costmaps, which work by separating the processing of costmap data into semantically-separated layers. Each layer tracks one type of obstacle or constraint, and then modifies a master costmap which is used for the path planning. We show how the algorithm can be integrated with the open-source ROS navigation stack, and how our approach is easier to fine-tune to specific environmental contexts than the existing monolithic one. Our design also results in faster path planning in practical use, and exhibits a cleaner separation of concerns that the original architecture. The new algorithm also makes it possible to represent complex cost values in order to create navigation behavior for a wide range of contexts.

Effective robot navigation in the presence of humans is hard. Not only do human obstacles move, they react to the movements of the robot according to instinct and social rules. In order to efficiently navigate around each other, both the robot and the human must move in a way that takes the other into account. Failure to do so can lead to a lowering of the perceived quality of the interaction and, more importantly, it can also delay one or both parties, causing them to be less efficient in whatever task they are trying to achieve. In this paper, we present a system capable of creating more efficient corridor navigation behaviors by manipulating existing navigation algorithms and introducing social cues from the robot to the human. We give the results of a user study, demonstrating the effectiveness of our system, and discuss how it can be applied more generally to a wide variety of situations.

Every year, tens of thousands of middle school and high school students participate in robotics competitions, such as Botball, FIRST, and VEX. This provides them with an excellent introduction to the ins and outs of building robots and programming them to autonomously accomplish specific tasks. However, the rules of many of these competitions often limit or prohibit human interaction with the robots. As a result, students are not exposed to and are, thus, not encouraged to think about human-robot interaction (HRI) and its potential impacts on society.

Polonius is a robot control interface designed for running Wizard of Oz style experiments. It is designed to be easy enough to be used by the non-programmer collaborators of roboticists. The program acts as an intermediary between the robot and a wizard interacting with a GUI based on a pre-defined script. Polonius also eliminates the need for coding the video after experiments by integrating a robust logging system.

Given the difficulty of social human-robot interaction (HRI), finding an appropriate conceptual model, as well as a useful venue to test the model, is key. While most work in social HRI draws insight and inspiration from the field of social psychology, this paper explores the philosophical backing and benefits of using ideas from theatre to frame social interactions. We present an analogy to Searle's Chinese Room argument to motivate the expressive challenges faced by human actors and by robots in social situations. We then compare the elements of theatre with the elements of HRI, and discuss techniques that we believe will lead to improved interactions.

MOTIVATION: The most accurate way to determine the intron-exon structures in a genome is to align spliced cDNA sequences to the genome. Thus, cDNA-to-genome alignment programs are a key component of most annotation pipelines. The scoring system used to choose the best alignment is a primary determinant of alignment accuracy, while heuristics that prevent consideration of certain alignments are a primary determinant of runtime and memory usage. Both accuracy and speed are important considerations in choosing an alignment algorithm, but scoring systems have received much less attention than heuristics. RESULTS: We present Pairagon, a pair hidden Markov model based cDNA-to-genome alignment program, as the most accurate aligner for sequences with high- and low-identity levels. We conducted a series of experiments testing alignment accuracy with varying sequence identity. We first created 'perfect' simulated cDNA sequences by splicing the sequences of exons in the reference genome sequences of fly and human. The complete reference genome sequences were then mutated to various degrees using a realistic mutation simulator and the perfect cDNAs were aligned to them using Pairagon and 12 other aligners. To validate these results with natural sequences, we performed cross-species alignment using orthologous transcripts from human, mouse and rat. We found that aligner accuracy is heavily dependent on sequence identity. For sequences with 100% identity, Pairagon achieved accuracy levels of >99.6%, with one quarter of the errors of any other aligner. Furthermore, for human/mouse alignments, which are only 85% identical, Pairagon achieved 87% accuracy, higher than any other aligner. AVAILABILITY: Pairagon source and executables are freely available at http://mblab.wustl.edu/software/pairagon/