HRI-DGDM: Dual-Graph Guided Diffusion Model for Uncertain Human Motion Modeling in HRI

Abstract

Human motion in human-robot interaction (HRI) is inherently uncertain, even when performing the same task repeatedly. This variability poses a significant challenge for prediction, as models must capture a distribution of plausible futures rather than a single deterministic trajectory. Traditional graph convolutional network based models, while effective at capturing spatial temporal dependencies, are fundamentally limited by their deterministic nature and struggle to represent this inherent motion uncertainty. To address this, diffusion models have emerged as a powerful framework for modeling uncertainty. However, their direct application to HRI is hindered by two key limitations: they often prioritize motion diversity over prediction accuracy, potentially generating physically implausible results, and they fail to adequately model the complex, multi-scale spatial temporal coupling between human and robot motions. To overcome these challenges, we propose HRI-DGDM, a HRI motion prediction framework based on a dual-graph guided diffusion model. Our method introduces a dual-graph structure—comprising a structural graph for kinematic priors and a collaboration graph learned from motion dynamics—to guide the denoising process with strong structural priors. A dedicated spatial temporal denoising network (STDN) fuses multi-scale features from both graphs through adaptive fusion and hierarchical spatial temporal modeling. Furthermore, a masking-based conditioning mechanism anchors the observed history during denoising, ensuring temporal consistency and preventing drift. Experiments on HRI scenarios demonstrate that HRI-DGDM outperforms baselines in prediction accuracy.