构建人助场景动作数据集并用扩散模型预测交互动作，提升机器人助人能力。
HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario
- 用条件Transformer+去噪扩散模型预测双人协作动作
- 在未见场景下仍保持高精度，优于基线方法
- 适合做智能助手机器人运动规划的研究者
劳动力短缺与人口老龄化推动了辅助机器人在照护场景中的需求。为实现安全、响应迅速的协助，机器人需准确预测物理交互中人类的动作。然而，由于助人场景差异大、交互动力学复杂，该任务仍具挑战。本文提出：（1）HHI-Assist数据集，包含多人协作助人动作的动捕片段；（2）基于条件Transformer的去噪扩散模型，用于预测交互双方的姿态。该模型能有效捕捉护理者与被照顾者之间的耦合动态，在多个未见场景中表现优异，显著优于基线方法，并具备强泛化能力。本工作推进了交互感知的动作预测，为提升机器人助人策略提供了新基础。数据集与代码已公开：https://sites.google.com/view/hhi-assist/home
原文摘要 · Abstract (English)
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
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