Expressive motion with x, y and theta: Laban Effort Features for mobile robots

摘要

There is a saying that 95% of communication is body language, but few robot systems today make effective use of that ubiquitous channel. Motion is an essential area of social communication that will enable robots and people to collaborate naturally, develop rapport, and seamlessly share environments. The proposed work presents a principled set of motion features based on the Laban Effort system, a widespread and extensively tested acting ontology for the dynamics of “how” we enact motion. The features allow us to analyze and, in future work, generate expressive motion using position (x, y) and orientation (theta). We formulate representative features for each Effort and parameterize them on expressive motion sample trajectories collected from experts in robotics and theater. We then produce classifiers for different “manners” of moving and assess the quality of results by comparing them to the humans labeling the same set of paths on Amazon Mechanical Turk. Results indicate that the machine analysis (41.7% match between intended and classified manner) achieves similar accuracy overall compared to a human benchmark (41.2% match). We conclude that these motion features perform well for analyzing expression in low degree of freedom systems and could be used to help design more effectively expressive mobile robots.