In December 2013, the Defense Advanced Research Projects Agency (DARPA) Robotics Challenge (DRC) Trials were held in Homestead, Florida. The DRC Trials were designed to test the capabilities of humanoid robots in disaster response scenarios with degraded communications. Each team created their own interaction method to control their robot, either the Boston Dynamics Atlas robot or a robot built by the team itself. Of the 15 competing teams, eight participated in our study of human-robot interaction. We observed the participating teams from the field (with the robot) and in the control room (with the operators), noting many performance metrics, such as critical incidents and utterances, and categorizing their interaction methods according to the number of operators, control methods, and amount of interaction. We decomposed each task into a series of subtasks, different from the DRC Trials official subtasks for points, to gain a better understanding of each team's performance in varying complexities of mobility and manipulation. Each team's interaction methods have been compared to their performance, and correlations have been analyzed to understand why some teams ranked higher than others. We discuss lessons learned from this study, and we have found in general that the guidelines for human-robot interaction for unmanned ground vehicles still hold true: more sensor fusion, fewer operators, and more automation lead to better performance. C 2015 Wiley Periodicals, Inc.Journal of Field Robotics DOI 10.1002/rob 422 • Journal of Field Robotics-2015 primary guidelines applicable to the design of HRI within the USAR domain:

In June 2015, the Defense Advanced Research Projects Agency (DARPA) Robotics Challenge (DRC) Finals were held in Pomona, California. The DRC Finals served as the third phase of the program designed to test the capabilities of semi-autonomous, remote humanoid robots to perform disaster response tasks with degraded communications. All competition teams were responsible for developing their own interaction method to control their robot. Of the 23 teams in the competition, 20 consented to participate in this study of human–robot interaction (HRI). The evaluation team observed the consenting teams during task execution in their control rooms (with the operators), and all 23 teams were observed on the field during the public event (with the robot). A variety of data were collected both before the competition and on-site. Each participating team’s interaction methods were distilled into a set of characteristics pertaining to the robot, operator strategies, control methods, and sensor fusion. Each task was decomposed into subtasks that were classified according to the complexity of the mobility and/or manipulation actions being performed. Performance metrics were calculated regarding the number of task attempts, performance time, and critical incidents, which were then correlated to each team’s interaction methods. The results of this analysis suggest that a combination of HRI characteristics, including balancing the capabilities of the operator with those of the robot and multiple sensor fusion instances with variable reference frames, positively impacted task performance. A set of guidelines for designing HRI with remote, semi-autonomous humanoid robots is proposed based on these results.

Objective The aim of this study is to determine the effects of a powered exoskeleton on measures of physical and cognitive performance. Background US warfighters carry heavy equipment into battle, and exoskeletons may reduce that burden. While exoskeletons are currently evaluated for their effects on physical performance, their cognitive effects are not currently considered. Method Twelve military members participated in a simulated patrol task under three conditions: wearing a powered exoskeleton (PWR), an unpowered exoskeleton (UNP), and without wearing an exoskeleton (OFF). While following a confederate over obstacles at a constant pace, participants performed additional audio and visual tasks. Dependent measures included visual misses, visual reaction time, audio misses, audio reaction time, incremental lag time, and NASA-TLX scores. Results The variability in the follow-task lag time was lowest with OFF and highest with UNP, highlighting reduced ability to maintain pace with the exoskeleton. Visual reaction time was significantly slower with PWR compared to OFF for 5 of 12 subjects. The NASA-TLX overall workload scores were lower for OFF compared to PWR and UNP. Conclusion Efforts to understand individual variability are warranted such that exoskeleton designs can be used for a wider set of the population. While not all subjects had measurable differences in the selected performance tasks, the perception of increased workload was present across subjects. It remains to be determined what difference in reaction time would be operationally relevant for task-specific settings. Application Findings draw attention to the need to consider “cognitive fit” and subject differences in the design and implementation of exoskeletons.

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Benchmarking of robotic manipulations is one of the open issues in robotic research. An important factor that has enabled progress in this area in the last decade is the existence of common object sets that have been shared among different research groups. However, the existing object sets are very limited when it comes to cloth-like objects that have unique particularities and challenges. This paper is a first step towards the design of a cloth object set to be distributed among research groups from the robotics cloth manipulation community. We present a set of household cloth objects and related tasks that serve to expose the challenges related to gathering such an object set and propose a roadmap to the design of common benchmarks in cloth manipulation tasks, with the intention to set the grounds for a future debate in the community that will be necessary to foster benchmarking for the manipulation of cloth-like objects. Some RGB-D and object scans are collected as examples for the objects in relevant configurations and shared in http://www.iri.upc.edu/groups/perception/ClothObjectSet/