A Scalable Approach to Predict Multi-Agent Motion for Human-Robot Collaboration

Abstract

Human motion prediction is considered a key component for enabling fluent human-robot collaboration. The ability to anticipate the motion and subsequent intent of the partner(s) remains a challenging task due to the complex and interpersonal nature of human behavior. In this work, we propose a novel sequence learning approach that learns a robust representation over the observed human motion and can condition future predictions over a subset of past sequences. Our approach works for both single and multi-agent settings and relies on an interpretable latent space that has the implicit benefit of improving human motion understanding. We evaluated the proposed approach by comparing its performance against state-of-the-art motion prediction methods on single, multi-agent, and human-robot collaboration datasets. The results suggest that our approach outperforms other methods over all the evaluated temporal horizons, for single-agent and multi-agent motion prediction. The improved performance of our approach for both single and multi-agent settings, coupled with an interpretable latent space, can enable close-proximity human-robot collaboration.