Computer science

One challenge in robotics is to develop motion planning tools that enable mobile robots to move safely and predictably in public spaces with people. Navigating public spaces requires a high degree of information about context and environment, which can be partly identified by understanding how people move. Motivated by research in dance and robotics, we developed an exploratory study of improvisational movement for dance performance. Dance forms have recognizable styles and specific interaction patterns (turn-taking, tempo changes, etc.) that reveal important information about behavior and context. Following extensive iterations with expert dancers, we developed a sequence of basic motion algorithms based on improvisation exercises to generate three unique, original performances between a robot and human performers trained in various dance styles. We developed a novel method for tracking dancers in real time using inputs to generate choreography for non-anthropomorphic robots. Although the motion algorithms were identical, the individual dancers generated vastly different performances and elicited unexpected motions and choreographies from the robot. We summarize our study and identify some challenges of devising performances between robots and humans, and outline future work to experiment with more advanced algorithms.

Tonight We Improvise!