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.

中文翻译：

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可预测人类机器人协作的多智能体运动的可扩展方法

人体运动预测被认为是实现流畅的人机协作的关键组成部分。由于人类行为的复杂性和人际关系，预测伙伴的运动和随后意图的能力仍然是一项具有挑战性的任务。在这项工作中，我们提出了一种新颖的序列学习方法，该方法可以学习观察到的人类运动的鲁棒表示，并可以对过去序列的子集进行未来的预测。我们的方法适用于单主体和多主体设置，并依赖于可解释的潜在空间，该潜在空间具有改善人体运动理解的隐含优势。通过将其性能与单，多代理和人机协作数据集上的最新运动预测方法进行比较，我们评估了该方法。结果表明，对于单智能体和多智能体运动预测，在所有评估的时间范围内，我们的方法均优于其他方法。我们针对单代理和多代理设置的方法的改进性能，加上可解释的潜在空间，可以实现近距离人机协作。