HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario
This work addresses the problem of enabling safe and responsive robotic assistance for care recipients, particularly in light of labor shortages and aging populations, though it is incremental in advancing interaction-aware motion prediction.
The paper tackles the challenge of accurate human motion prediction for assistive robots by introducing HHI-Assist, a dataset of human-human interactions, and a conditional Transformer-based denoising diffusion model, which shows improvements over baselines and generalizes to unseen scenarios.
The increasing labor shortage and aging population underline the need for assistive robots to support human care recipients. To enable safe and responsive assistance, robots require accurate human motion prediction in physical interaction scenarios. However, this remains a challenging task due to the variability of assistive settings and the complexity of coupled dynamics in physical interactions. In this work, we address these challenges through two key contributions: (1) HHI-Assist, a dataset comprising motion capture clips of human-human interactions in assistive tasks; and (2) a conditional Transformer-based denoising diffusion model for predicting the poses of interacting agents. Our model effectively captures the coupled dynamics between caregivers and care receivers, demonstrating improvements over baselines and strong generalization to unseen scenarios. By advancing interaction-aware motion prediction and introducing a new dataset, our work has the potential to significantly enhance robotic assistance policies. The dataset and code are available at: https://sites.google.com/view/hhi-assist/home