Beyond Speculative Robot Ethics

In this article we develop a dialogue model for robot technology experts and designated users to discuss visions on the future of robotics in long-term care. Our vision assessment study aims for more distinguished and more informed visions on future robots. Surprisingly, our experiment also lead to some promising co-designed robot concepts in which jointly articulated moral guidelines are embedded. With our model we think to have designed an interesting response on a recent call for a less speculative ethics of technology by encouraging discussions about the quality of positive and negative visions on the future of robotics.

Tactile Interactions with a Humanoid Robot : Novel Play Scenario Implementations with Children with Autism

Acknowledgments: This work has been partially supported by the European Commission under contract number FP7-231500-ROBOSKIN. Open Access: This article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.The work presented in this paper was part of our investigation in the ROBOSKIN project. The project has developed new robot capabilities based on the tactile feedback provided by novel robotic skin, with the aim to provide cognitive mechanisms to improve human-robot interaction capabilities. This article presents two novel tactile play scenarios developed for robot-assisted play for children with autism. The play scenarios were developed against specific educational and therapeutic objectives that were discussed with teachers and therapists. These objectives were classified with reference to the ICF-CY, the International Classification of Functioning – version for Children and Youth. The article presents a detailed description of the play scenarios, and case study examples of their implementation in HRI studies with children with autism and the humanoid robot KASPAR.Peer reviewedFinal Published versio

Power moves beyond complementarity: A staring look elicits avoidance in low power perceivers and approach in high power perceivers

Sustained, direct eye-gaze — staring — is a powerful cue that elicits strong responses in many primate and non-primate species. The present research examined whether fleeting experiences of high and low power alter individuals’ spontaneous responses to the staring gaze of an onlooker. We report two experimental studies showing that sustained, direct gaze elicits spontaneous avoidance tendencies in low power perceivers, and spontaneous approach tendencies in high power perceivers. These effects emerged during interactions with different targets and when power was manipulated between-individuals (Study 1) and within-individuals (Study 2), thus attesting to a high degree of flexibility in perceivers’ reactions to gaze cues. Together, the present findings indicate that power can break the cycle of complementarity in individuals’ spontaneous responding: low power perceivers complement and move away from, and high power perceivers reciprocate and move towards, staring onlookers

A survey on policy search algorithms for learning robot controllers in a handful of trials

Most policy search algorithms require thousands of training episodes to find an effective policy, which is often infeasible with a physical robot. This survey article focuses on the extreme other end of the spectrum: how can a robot adapt with only a handful of trials (a dozen) and a few minutes? By analogy with the word "big-data", we refer to this challenge as "micro-data reinforcement learning". We show that a first strategy is to leverage prior knowledge on the policy structure (e.g., dynamic movement primitives), on the policy parameters (e.g., demonstrations), or on the dynamics (e.g., simulators). A second strategy is to create data-driven surrogate models of the expected reward (e.g., Bayesian optimization) or the dynamical model (e.g., model-based policy search), so that the policy optimizer queries the model instead of the real system. Overall, all successful micro-data algorithms combine these two strategies by varying the kind of model and prior knowledge. The current scientific challenges essentially revolve around scaling up to complex robots (e.g., humanoids), designing generic priors, and optimizing the computing time.Comment: 21 pages, 3 figures, 4 algorithms, accepted at IEEE Transactions on Robotic

Facial Expression Rendering in Medical Training Simulators: Current Status and Future Directions

Recent technological advances in robotic sensing and actuation methods have prompted development of a range of new medical training simulators with multiple feedback modalities. Learning to interpret facial expressions of a patient during medical examinations or procedures has been one of the key focus areas in medical training. This paper reviews facial expression rendering systems in medical training simulators that have been reported to date. Facial expression rendering approaches in other domains are also summarized to incorporate the knowledge from those works into developing systems for medical training simulators. Classifications and comparisons of medical training simulators with facial expression rendering are presented, and important design features, merits and limitations are outlined. Medical educators, students and developers are identified as the three key stakeholders involved with these systems and their considerations and needs are presented. Physical-virtual (hybrid) approaches provide multimodal feedback, present accurate facial expression rendering, and can simulate patients of different age, gender and ethnicity group; makes it more versatile than virtual and physical systems. The overall findings of this review and proposed future directions are beneficial to researchers interested in initiating or developing such facial expression rendering systems in medical training simulators.This work was supported by the Robopatient project funded by the EPSRC Grant No EP/T00519X/