An Intervening Ethical Governor for a Robot Mediator in Patient-Caregiver Relationships

© Springer International Publishing AG 2015DOI: 10.1007/978-3-319-46667-5_6Patients with Parkinson’s disease (PD) experience challenges when interacting with caregivers due to their declining control over their musculature. To remedy those challenges, a robot mediator can be used to assist in the relationship between PD patients and their caregivers. In this context, a variety of ethical issues can arise. To overcome one issue in particular, providing therapeutic robots with a robot architecture that can ensure patients’ and caregivers’ dignity is of potential value. In this paper, we describe an intervening ethical governor for a robot that enables it to ethically intervene, both to maintain effective patient–caregiver relationships and prevent the loss of dignity

The Benefits of Robot Deception in Search and Rescue: Computational Approach for Deceptive Action Selection via Case-Based Reasoning

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.DOI: 10.1109/SSRR.2015.7443002By increasing the use of autonomous rescue robots in search and rescue (SAR), the chance of interaction between rescue robots and human victims also grows. More specifically, when autonomous rescue robots are considered in SAR, it is important for robots to handle sensitively human victims’ emotions. Deception can potentially be used effectively by robots to control human victims’ fear and shock as used by human rescuers. In this paper, we introduce robotic deception in SAR contexts and present a novel computational approach for an autonomous rescue robot’s deceptive action selection mechanism

The benefits of other-oriented robot deception in human-robot interaction

Deception is an essential social behavior for humans, and we can observe human deceptive behaviors in a variety of contexts including sports, culture, education, war, and everyday life. Deception is also used for the purpose of survival in animals and even in plants. From these findings, it is obvious that deception is a general and essential behavior for any species, which raises an interesting research question: can deception be an essential characteristic for robots, especially social robots? Based on this curiosity, this dissertation aimed to develop a robot's deception capabilities, especially in human-robot interaction (HRI) situations. Specifically, the goal of this dissertation is to develop a social robot's deceptive behaviors that can produce benefits for the deceived humans (other-oriented robot deception). To achieve other-oriented robot deception, several scientific contributions were accomplished in this dissertation. A novel taxonomy of robot deception was defined, and a general computational model for a robot's deceptive behaviors was developed based on criminological law. Appropriate HRI contexts in which a robot's other-oriented deception can generate benefits were explored, and a methodology for evaluating a robot's other-oriented deception in appropriate HRI contexts was designed, and studies were conducted with human subjects. Finally, the ethical implications of other-oriented robot deception were also explored and thoughtfully discussed.Ph.D

Other-Oriented Robot Deception: A Computational Approach for Deceptive Action Generation to Benefit the Mark

Social robots can benefit by adding deceptive capabilities. In particular, robotic deception should benefit the deceived human partners when used in the context of human-robot interaction (HRI). We define this kind of robotic deception as a robot’s other-oriented deception and aimed to add these capabilities to the robotic systems. Toward that end, we develop a computational model inspired by criminological definition of deception. In this paper, we establish a definition of other-oriented robotic deception in HRI and present a novel model that can enable a humanoid robot to autonomously generate other-oriented deceptive actions during the interaction

Robot Deception and Squirrel Behavior: A Case Study in Bio-inspired Robotics

A common behavior in animals and human beings is deception. Deceptive behavior in robotics is potentially beneficial in several domains ranging from the military to a more everyday context. In our research, novel algorithms were developed for the deceptive behavior of a robot, inspired by the observed deceptive behavior of squirrels for cache protection strategies, then evaluating the results via simulation studies. In this paper, we present this bio-inspired algorithm for robot deception and observe whether the algorithm is truly applicable in actual robot systems

Learning Tasks and Skills Together From a Human Teacher

We are interested in developing Learning from Demonstration (LfD) systems that are tailored to be used by everyday people. We highlight and tackle the issues of skill learning, task learning and interaction in the context of LfD As part of the AAAI 2011 LfD Challenge, we will demonstrate some of our most recent Socially Guided-Machine Learning work, in which the PR2 robot learns both low-level skills and high-level tasks through an ongoing social dialog with a human partne