Vocalics is the study of the nonverbal aspects of speech, such as volume, pitch, and rate. Our contribution is a parametric We present an overview of the control, recognition, decision-making, vocalic behavior controller that autonomously adjusts and learning techniques utilized by the Interaction the robot speaker volume based on models of how a Lab (robotics.usc.edu/interaction) at the University human user will hear speech produced by the robot. These of Southern California (USC) to enable autonomy in sociable models vary with distance, orientation, and perceived environmental and socially assistive robots. These techniques are implemented interference (Mead & Matarić 2014). Our future with two software libraries: 1) the Social Behavior work will investigate adapting the pitch and rate of speech Library (SBL) provides autonomous social behavior produced by a robot to improve user speech perception.

It has been demonstrated that people have a tendency to adapt both their linguistic representations and physical Communication is often required between agents as they actions in response to those they are interacting with, i.e., attempt to solve collaborative multi-agent tasks. This is they tend to formulate behavior and speech that will be particularly true in conditions in which an agent is working salient and sensible to a collaborating partner (Whittaker alongside a human--clearly, conventional electronic 2003). Collaboration in humans occurs via a process in communication is not feasible in this scenario; rather, these which people align their linguistic representations of the agents, including humans, must take advantage of physical environment allowing for more effective communicative communication in the shared context to confer necessary behavior. This alignment is achieved via a process in information. As an agent observes the actions of the others, which local alignment of environmental representations, it must modify its own behavior accordingly.

In an attempt to solve as much of the AAAI Robot Challenge as possible, five research institutions representing academia, industry, and government integrated their research into a single robot named GRACE. This article describes this first-year effort by the GRACE team, including not only the various techniques each participant brought to GRACE but also the difficult integration effort itself.

In an attempt to solve as much of the AAAI Robot Challenge as possible, five research institutions representing academia, industry, and government integrated their research into a single robot named GRACE. This article describes this first-year effort by the GRACE team, including not only the various techniques each participant brought to GRACE but also the difficult integration effort itself.