数据有效的人类意图预测：利用神经网络验证和专家指导
Data Efficient Human Intention Prediction: Leveraging Neural Network Verification and Expert Guidance
摘要：
预测人类意图对于促进安全和高效的人机协作（HRC）至关重要。然而，建立数据驱动的人类意图预测模型具有挑战性。一个主要的挑战是由于人体运动数据的多样性和噪声。收集全面覆盖所有可能场景的海量运动数据集是昂贵的，这导致在某些场景下人体运动数据的稀缺，因此，在构建鲁棒且可靠的意图预测器方面造成困难。为了应对这一挑战，本文提出了一种迭代对抗数据增强（IADA）框架，用于从不足的训练数据中学习神经网络模型。该方法使用神经网络验证来识别最“混乱”的输入样本，并利用专家指导来安全地和迭代地用这些样本来增强训练数据。所提出的框架适用于收集的人类数据集。实验结果表明，与现有的训练方法相比，我们的方法可以实现更强大和准确的预测性能。
英文摘要：
Predicting human intention is critical to facilitating safe and efficient human-robot collaboration (HRC). However, it is challenging to build data-driven models for human intention prediction. One major challenge is due to the diversity and noise in human motion data. It is expensive to collect a massive motion dataset that comprehensively covers all possible scenarios, which leads to the scarcity of human motion data in certain scenarios, and therefore, causes difficulties in constructing robust and reliable intention predictors. To address the challenge, this paper proposes an iterative adversarial data augmentation (IADA) framework to learn neural network models from an insufficient amount of training data. The method uses neural network verification to identify the most "confusing" input samples and leverages expert guidance to safely and iteratively augment the training data with these samples. The proposed framework is applied to collected human datasets. The experiments demonstrate that our method can achieve more robust and accurate prediction performance compared to existing training methods.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
