When it’s between a robot on your team and a human member of a competing team, who will you favor? Past research indicates that people favor and behave more morally toward ingroup than outgroup members. Conversely, people typically indicate that they have more moral responsibilities toward humans than nonhumans. This study puts participants into two competing teams, each consisting of two humans and two robots, to examine how people behave toward others depending on Group (ingroup, outgroup) and Agent (human, robot) variables. Measures of behavioral aggression used in previous studies (i.e., noise blasts) and reported liking and anthropomorphism evaluations of humans and robots indicated that participants favored the ingroup over the outgroup, and humans over robots. Group had a greater effect than Agent, so participants preferred ingroup robots to outgroup humans.

The insufficient level of reproducibility of published experimental results has been identified as a core issue in the field of robotics in recent years. Why is that? First of all, robotics focuses on the abstract concept of computation and the creation of technological artifacts, i.e., software that implements these concepts. Hence, before actually reproducing an experiment, the subject of investigation must be artificially created, which is non-trivial given the inherent complexity [5]. Second, robotics experiments usually include expensive and often customized hardware setups (robots), that are difficult to operate for non-experts. Finally, there is no agreed upon set of methods in order to setup, execute, or (re-)conduct an experiment. To this end, we introduce an interdisciplinary and geographically distributed collaboration project that aims at implementing good experimental methodology in interdisciplinary robotics research with respect to: a) reproducibility of required technical artifacts, b) explicit and comprehensible experiment design, c) repeatable/reproducible experiment execution, and d) reproducible evaluation of obtained experiment data. The ultimate goal of this collaboration is to reproduce the same experiment in two different laboratories using the same systematic approach which is presented in this work.

Knowing accurately the kinematic and dynamic parameters of a manipulated object is required for common coordination strategies in physical human-robot interaction. Parameter bias may disturb the human during interaction and bias the recognition of the human motion intention. This work presents a strategy allowing the tracking of human motion and inducing the motions necessary for identification. Such motions are projected in the null space of the partial grasp matrix, relating the human and the robot redundant motion directions, to avoid the disturbance of the human motion. The approach is evaluated in a human-robot object manipulation setting.

The classification of nonstationary signals in a noisy environment is a difficult task. In this paper a modified version of S-Transform technique has been proposed for classification of power signal disturbances. The S-Transform is a signal processing technique which is used for visual localization, detection, pattern classification. S-Transform has good ability in gathering high frequency signals and suppressing the lower frequency signal. The S-Transform has been used to extract features from the nonstationary power disturbance signals. The extracted features are fed as the input support vector machine classifier for power signal disturbance pattern classification. To enhance the pattern classification accuracy the extreme learning