HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario

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 th...

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Vydáno v:IEEE robotics and automation letters Ročník 10; číslo 9; s. 8746 - 8753
Hlavní autoři: Saadatnejad, Saeed, Hosseininejad, Reyhaneh, Barreiros, Jose, Tsui, Katherine M., Alahi, Alexandre
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.09.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Data sets for robot learning
Datasets
Diffusion models
Dynamics
Human motion
Human-robot interaction
intention recognition
Motion capture
Noise reduction
physical human-robot interaction
Predictive models
Receivers
Robot dynamics
Robots
Service robots
Skeleton
Transformers
ISSN:2377-3766, 2377-3766
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Shrnutí: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.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2025.3586011