Legible Robot Motion from Conditional Generative Models

Matthew Bronars, Danfei Xu

Keywords: Human Robot Interaction, Generative Modeling, Legible Motion, Learning from Demonstrations

TL;DR: We introduce Generative Legible Motion Models (GLMM), a framework that utilizes conditional generative models to learn legible trajectories from human demonstrations.

Abstract:

In human robot collaboration, legible motion that
clearly conveys its intentions and goals is essential. This is because forecasting a robot’s next
move can lead to an improved user experience,
safety, and task efficiency. Current methods for
generating legible motion utilize hand designed
cost functions and classical motion planners, but
there is need for data driven policies that are
trained end-to-end on demonstration data. In
this paper we propose Generative Legible Motion Models (GLMM), a framework that utilizes
conditional generative models to learn legible trajectories from human demonstrations. We find
that GLMM produces motion that is 76% more
legible than standard goal conditioned generative
models and 83% percent more legible than generative models without goal conditioning.

Submission Number: 46