A robot mediator can enhance the quality of patient care in a health care context. Patients with Parkinson's 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.

Previously, our lab has hypothesized that a peripheral social robot may be able to help uphold the dignity of Parkinson's patients who are stigmatized by their caregivers. The presence of a robotic agent is liable to influence the patient-caregiver relationship. Patient self-disclosure is a key element of a healthy patient-caregiver relationship. This new study examined how the apparent attentiveness of a peripheral robot influences personal disclosure during a scripted interview. The study did not draw from a patient-caregiver population and was conducted as a Wizard of Oz study. The attentiveness of the robot did not make a difference in the interviewees' depth of disclosure. Self-report measures indicated a difference between the attentive robot condition and the other two conditions when participants were asked if they felt like the robot was listening to them.

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.

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 partner

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.

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.