An intelligent design interface for dancers to teach robots

Abstract

Dancers are human Expressive Motion experts and could theoretically help robots communicate their state to people, e.g., rushed, confused, curious. The problem is twofold: first, dancers are trained in human-motion whereas many robots are non-anthropomorphic, and second, most dancers are not programmers. This is where the present interface is useful: the robot demos a batch of motions, in person, and the dancer, who knows expressive motion when she sees it, rates each path's success at communicating a particular state. Using an evolutionary algorithm, the interface - where feedback is recorded on the robot's screen and motion is demonstrated via the robot - calculates a new batch of motions that explore variations of the top-rated paths from the previous generation. This approach addresses the challenges of visualizing the expressive potential of non-anthropomorphic robots, while also ensuring path characteristics are reproducible via the robot's motion controller. The purpose of the interface is to help a non-expert negotiate a high-dimensional space of robot motion expression. Thus, it also has interactive functionality enabling users to freeze a feature value they like, or reset all features to begin again. To illustrate the system, this paper includes the results of two dancers designing motions for an omni-directional mobile robot, showing convergence with every generation. In reality, motion designers may have many authoring styles - exploring multiple solutions before honing in, or being satisfied easily versus getting each detail exactly right. By combining human-in-the-loop machine learning with direct authoring, we create a kinetic conversation between the robot and the dancer, and gain the ability to model knowledge from complementary fields.