Jaeeun Shim

A Taxonomy of Robot Deception and Its Benefits in HRI

Deception is a common and essential behavior in humans. Since human beings gain many advantages from deceptive capabilities, we can also assume that robotic deception can provide benefits in several ways. Particularly, the use of robotic deception in human-robot interaction contexts is becoming an important and interesting research question. Despite its importance, very little research on robot deception has been conducted. Furthermore, no basic metrics or definitions of robot deception have been proposed yet. In this paper, we review the previous work on deception in various fields including psychology, biology, and robotics and will propose a novel way to define a taxonomy of robot deception. In addition, we will introduce an interesting research question of robot deception in HRI contexts and discuss potential approaches.

Biologically-Inspired Deceptive Behavior for a Robot

A common behavior in animals or human beings is deception. We focus on deceptive behavior in robotics because the appropriate use of deception is beneficial in several domains ranging from the military to a more everyday context. In this research, novel algorithms are developed for the deceptive behavior of a robot, inspired by the observed deceptive behavior of squirrels for cache protection strategies, evaluating the results via simulation studies.

Dance dance Pleo: developing a low-cost learning robotic dance therapy aid

In this paper, a low cost system for child interaction through turn taking and dance based on the Pleo robot platform is presented. This system is easily taught new dance movements through visual and haptic cues and provides immediate feedback of the learned motion, making it possible for individuals unfamiliar with robotics programming to alter its behavior through natural interaction.

Human-like action segmentation for option learning

Robots learning interactively with a human partner has several open questions, one of which is increasing the efficiency of learning. One approach to this problem in the Reinforcement Learning domain is to use options, temporally extended actions, instead of primitive actions. In this paper, we aim to develop a robot system that can discriminate meaningful options from observations of human use of low-level primitive actions. Our approach is inspired by psychological findings about human action parsing, which posits that we attend to low-level statistical regularities to determine action boundary choices. We implement a human-like action segmentation system for automatic option discovery and evaluate our approach and show that option-based learning converges to the optimal solutions faster compared with primitive-action-based learning.

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 robots other-oriented deception and aim 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.

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

Patients 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.

GeneTracer: Gene sequence analysis of disease mutations VAST 2010 mini challenge 3 award: Excellent process explanation

Our visual analytics tool GeneTracer, developed for the VAST 2010 genetic sequence mini challenge, visualizes gene sequences of current outbreaks and native sequences along with disease characteristics. We successfully used GeneTracer in combination with data mining techniques to solve the challenge.

An intervening ethical governor for a robot mediator in patient-caregiver relationship: Implementation and evaluation

A robot mediator can enhance the quality of patient care in a health care context. Patients with Parkinsons disease can experience difficulties in precisely expressing their emotions due to the loss of control of their facial musculature, leading to their stigmatization by caregivers. To remedy this challenge, a robot mediator can be inserted into a patient-caregiver relationship. In this context, it is essential to handle the ethical issues of neglect to ensure human dignity. In an earlier paper [19], we proposed an intervening ethical governor (IEG) model, which enables a robot to ethically intervene in a situation where patients or caregivers go across accepted ethical boundaries. In this paper, we show how the IEG model can be implemented and applied in a real robotics system. In addition, by conducting interviews with the target population (adults 60 years of age or older), we evaluate the current intervention rules in the model, discuss potential improvements to the model, and consider uses of the model in real clinical contexts.

Other-Oriented Robot Deception: How Can a Robot’s Deceptive Feedback Help Humans in HRI?

Deception is a common and essential behavior of social agents. By increasing the use of social robots, the need for robot deception is also growing to achieve more socially intelligent robots. It is a goal that robot deception should be used to benefit humankind. We define this type of benevolent deceptive behavior as other-oriented robot deception. In this paper, we explore an appropriate context in which a robot can potentially use other-oriented deceptive behaviors in a beneficial way. Finally, we conduct a formal human-robot interaction study with elderly persons and demonstrate that using other-oriented robot deception in a motor-cognition dual task can benefit deceived human partners. We also discuss the ethical implications of robot deception, which is essential for advancing research on this topic.