The generalizability of empirical research depends on the reproduction of findings across settings and populations. Consequently, generalizations demand resources beyond that which is typically available to any one laboratory. With collective interest in the joint Simon effect (JSE) -- a phenomenon that suggests people work more effectively with humanlike (as opposed to mechanomorphic) robots -- we pursued a multi-institutional research cooperation between robotics researchers, social scientists, and software engineers. To evaluate the robustness of the JSE in dyadic human-robot interactions, we constructed an experimental infrastructure for exact, lab-independent reproduction of robot behavior. Deployment of our infrastructure across three institutions with distinct research orientations (well-resourced versus resource-constrained) provides initial demonstration of the success of our approach and the degree to which it can alleviate technical barriers to HRI reproducibility. Moreover, with the three deployments situated in culturally distinct contexts (Germany, the U.S. Midwest, and the Mexico-U.S. Border), observation of a JSE at each site provides evidence its generalizability across settings and populations.

In this paper, we present our humanoid robot "Meka", participating in a multi party human robot dialogue scenario. Active arbitration of the robot's attention based on multi-modal stimuli is utilised to observe persons which are outside of the robots field of view. We investigate the impact of this attention management and addressee recognition on the robot's capability to distinguish utterances directed at it from communication between humans. Based on the results of a user study, we show that mutual gaze at the end of an utterance, as a means of yielding a turn, is a substantial cue for addressee recognition. Verification of a speaker through the detection of lip movements can be used to further increase precision. Furthermore, we show that even a rather simplistic fusion of gaze and lip movement cues allows a considerable enhancement in addressee estimation, and can be altered to adapt to the requirements of a particular scenario.

In recent years, an attempt is being made to control robots more intuitive and intelligible by exploiting and integrating anthropomorphic features to boost social human-robot interaction. The design and construction of anthropomorphic robots for this kind of interaction is not the only challenging issue -- smooth and expectation-matching motion control is still an unsolved topic. In this work we present a highly configurable, portable, and open control framework that facilitates anthropomorphic motion generation for humanoid robot heads by enhancing state-of-the-art neck-eye coordination with human-like eyelid saccades and animation. On top of that, the presented framework supports dynamic neck offset angles that allow animation overlays and changes in alignment to the robots communication partner whileretaining visual focus on a given target. In order to demonstrate the universal applicability of the proposed ideas we used this framework to control the Flobi and the iCub robot head, both in simulation and on the physical robot. In order to foster further comparative studies of different robot heads, we will release all software, based on this contribution, under an open-source license.

We present RoboBench, a novel platform for sharing robot full-system simulations for benchmarking. The creation of this platform and benchmark suite is motivated by a need for reproducible research. A challenge in creating a full-system benchmarks are incompatibilities in software created by different groups and the difficulty of reproducing software environments. We solve this problem by using software containers, an emerging virtualization technology. RoboBench enables sharing robot software in a runnable state, capturing the software behavior of robots carrying out missions. These simulations make clear the performance impact and resource usage of programs and algorithms relative to other software involved in the mission. These containers are integrated with the CITK platform for reproducible research, which automates generation and publishing of the containers. We present an overview of the system, a description of our prototype set of benchmark missions, along with a validation study comparing the computational load profile of a mission performed on a real and simulated robot. Additionally, we present preliminary results of an overall analysis of the benchmarks in the RoboBench suite, showing where computational work is expended in robotics common robotics tasks. RoboBench is extensible, and is the first step toward a robust, quantitative approach to engineering computationally-efficient robots.

Even though research on autonomous robots and human-robot interaction accomplished great progress in recent years, and reusable soft- and hardware components are available, many of the reported findings are only hardly reproducible by fellow scientists. Usually, reproducibility is impeded because required information, such as the specification of software versions and their configuration, required data sets, and experiment protocols are not mentioned or referenced in most publications. In order to address these issues, we recently introduced an integrated tool chain and its underlying development process to facilitate reproducibility in robotics. In this contribution we instantiate the complete tool chain in a unique user study in order to assess its applicability and usability. To this end, we chose three different robotic systems from independent institutions and modeled them in our tool chain, including three exemplary experiments. Subsequently, we asked twelve researchers to reproduce one of the formerly unknown systems and the associated experiment. We show that all twelve scientists were able to replicate a formerly unknown robotics experiment using our tool chain.

Autonomous robots are highly relevant targets for interaction studies, but can exhibit behavioral variability that confounds experimental validity. Currently, testing on real systems is the only means to prevent this, but remains very labour-intensive and often happens too late. To improve this situation, we are working towards early testing by means of partial simulation, with automated assessment, and based upon continuous software integration to prevent regressions. We will introduce the concept and describe a proof-of-concept that demonstrates fast feedback and coherent experiment results across repeated trials.

Simulated environments usually provide the most frequent test environment for robotic systems, often due to their cost and availability advantages. The crucial question is: how precisely must a simulation match the real world in order to produce realistic results? In this demonstration the humanoid robot head Flobi, as well as its simulation, will react to diverse visual stimuli in order to indicate visual attention which is an important cue in social interaction. We directly compare the behavior of the virtual robot, triggered by simulated stimuli, to the physical robot, triggered by real world stimuli. Therefore, we are able to assess and demonstrate the current limitations and advantages of our simulation compared to real world interactions.

A key issue for interactive robots is smooth and natural motion. When robots are tele-operated, e.g. for tele-presence or during “Wizard of Oz” style studies, this requires an intuitive and responsive control interface. Furthermore, this interface should not interfere with perceiving the robot's sensors.