Organizers: Amy Laviers, Kayhan Ozcimder
There is a growing interest in literature to study bio-inspired multi-agent robotic systems that involve individual level interactions in order to achieve a desired shared task. In human agents, these interactions are both verbal and nonverbal, and this poses unique challenges for moving machines, which will inherently be communicating along this nonverbal channel in human-facing scenarios (intentionally or not). Thus, a major subset of these studies explore the language used by human agents that guides the creation of expressive movement phrases in collective motion as well as communication. This tutorial/workshop hybrid addresses some of the challenges faced in these explorations with an interactive two track discussions. In the first track, we will begin the discussion by proposing a new framework, dance, to study movement and collective behavior. We will show that dance is an accessible medium to formally study decision-making and communication strategies of individuals in a group and the rules extracted from dance can be used to construct new formal methods of cooperation and decision-making for multi-agent robotic systems. In the second track, we will `zoom in' to discuss how to modulate the individual level interactions through movement by proposing, Labanotation, a rich notational system for transcribing and analyzing movement. In particular, we will show how Laban/Bartenieff Movement Studies (LBMS) provides a taxonomy as well as a field of trained practitioners who are experts in developing motion profiles with specific meaning and intention. Finally, the session will conclude with an interactive activity that merges these two approaches.