Roboethics: Ethics Applied to Robotics

This special issue deals with the emerging debate on robo- ethics, the human ethics ap- plied to robotics. Is a specific ethic applied to robotics truly neces- sary? Or, conversely, are not the gen- eral principles of ethics adequate to answer many of the issues raised by our field’s applications? In our opin- ion, and according to many roboticists and human scientists, many novel issues that emerge and many more that will show up in the immediate future, arising from the upcoming marketed robotics products, demand the development of new cultural and legal tools that can provide the crucial answers to the most sensitive questions

Quantifying Demonstration Quality for Robot Learning and Generalization

Learning from Demonstration (LfD) seeks to democratize robotics by enabling diverse end-users to teach robots to perform a task by providing demonstrations. However, most LfD techniques assume users provide optimal demonstrations. This is not always the case in real applications where users are likely to provide demonstrations of varying quality, that may change with expertise and other factors. Demonstration quality plays a crucial role in robot learning and generalization. Hence, it is important to quantify the quality of the provided demonstrations before using them for robot learning. In this paper, we propose quantifying the quality of the demonstrations based on how well they perform in the learned task. We hypothesize that task performance can give an indication of the generalization performance on similar tasks. The proposed approach is validated in a user study (N = 27). Users with different robotics expertise levels were recruited to teach a PR2 robot a generic task (pressing a button) under different task constraints. They taught the robot in two sessions on two different days to capture their teaching behaviour across sessions. The task performance was utilized to classify the provided demonstrations into high-quality and low-quality sets. The results show a significant Pearson correlation coefficient (R = 0.85, p < 0.0001) between the task performance and generalization performance across all participants. We also found that users clustered into two groups: Users who provided high-quality demonstrations from the first session, assigned to the fast-adapters group, and users who provided low-quality demonstrations in the first session and then improved with practice, assigned to the slow-adapters group. These results highlight the importance of quantifying demonstration quality, which can be indicative of the adaptation level of the user to the task

Autonomous Person-Specific Following Robot

Following a specific user is a desired or even required capability for service robots in many human-robot collaborative applications. However, most existing person-following robots follow people without knowledge of who it is following. In this paper, we proposed an identity-specific person tracker, capable of tracking and identifying nearby people, to enable person-specific following. Our proposed method uses a Sequential Nearest Neighbour with Thresholding Selection algorithm we devised to fuse together an anonymous person tracker and a face recogniser. Experiment results comparing our proposed method with alternative approaches showed that our method achieves better performance in tracking and identifying people, as well as improved robot performance in following a target individual

Enhancing collaborative human-robot interaction through physiological-signal based communication

status: publishe

On Line -Affective State Reporting Device: A Tool for Evaluating Affective State Inference Systems

ABSTRACT The monitoring of human affective state is a key part of developing responsive and naturally behaving human-robot interaction systems. However, evaluation and calibration of physiologically monitored affective state data is typically done using offline questionnaires and user reports. In this paper we investigate the use of an online-device for collecting real-time user reports of affective state during interaction with a robot. These reports are compared to both previous survey reports taken after the interaction, and the affective states estimated by an inference system. The aim is to evaluate and characterize the physiological signal-based inference system and determine which factors significantly influence its performance. This analysis will be used in future work, to fine tune the affective estimations by identifying what kind of variations in physiological signals precede or accompany the variations in reported affective states

Design for Wellbeing

There is a growing need for engineering designers to engage in creative activities that result in innovative products and technologies for the benefit of society. However, from an engineering perspective, issues of ‘life quality’ are currently heavily under-prioritized, particularly with regard to people with disabilities. This paper argues that both needs and solutions are now part of the designer’s responsibility, and that it is crucial to make a qualitative assessment of both the potential market impact and the ‘quality of life’ improvements afforded by innovations. Design for Wellbeing offers a perspective on life quality that goes beyond the traditional scope of assistive technology in that it aims to help people make a transformation from an actual state of being to a desired state of being – regardless of ability level