(Georgia Institute of Technology, 2014)
Arkin, Ronald C.; Moshkina, Lilia
More and more, robots are expected to interact with humans in a social, easily understandable manner, which presupposes effective use of robot affect. This chapter provides a
brief overview of
research advances into this important aspect of human-robot interaction.
(Georgia Institute of Technology, 2010)
Moshkina, Lilia; Park, Sunghyun; Arkin, Ronald C.; Lee, Jamee K.; Jung, HyunRyong
This paper describes the design of a complex time-varying affective software architecture. It is an expansion of the TAME architecture (Traits, Attitudes, Moods, and Emotions) as applied to humanoid robotics. In particular it is intended to promote effective human-robot interaction by conveying the robot’s affective state to the user in an easy-to-interpret manner.
(Georgia Institute of Technology, 2010)
Park, Sunghyun; Moshkina, Lilia; Arkin, Ronald C.
The design and implementation of mood as an
affective component for robotic behavior is described in the
context of the TAME framework – a comprehensive,
time-varying affective model for robotic behavior that
encompasses personality traits, attitudes, moods, and emotions.
Furthermore, a method for continuously adapting TAME’s
Mood component (and thereby the overall affective system) to
individual preference is explored by applying Learning
Momentum, which is a parametric adjustment learning
algorithm that has been successfully applied in the past to
improve navigation performance in real-time, reactive robotic
systems
(Georgia Institute of Technology, 2010)
Park, Sunghyun; Moshkina, Lilia; Arkin, Ronald C.
This paper describes the addition of nonverbal affective behaviors to a
humanoid robot, as well as recognition of these behaviors based on an online
survey. The expressive behaviors were implemented in the context of a framework
for affective robot behavior (TAME) and span across three types of affective
phenomena: traits, moods and emotions.