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arXiv 2407.01593cs.ROcs.LG
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neuROSym：用于人体运动预测的基于 ROS 的神经符号模型的部署与评估

neuROSym: Deployment and Evaluation of a ROS-based Neuro-Symbolic Model for Human Motion Prediction

School of Computer Science, University of Lincoln（林肯大学计算机科学学院）；Dept. of Information Engineering, University of Padua（帕多瓦大学信息工程系）

Sariah Mghames, Luca Castri, Marc Hanheide, Nicola Bellotto

备注：
6 pages, paper accepted to IEEE CIS-RAM 2024 conference (https://www.cis-ram.org/2024/). arXiv admin note: text overlap with arXiv:2304.11740
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AI总结：

本文提出基于 ROS 的 neuROSym 软件包，将 NeuroSyM 神经符号运动预测架构部署到 TIAGo 机器人，并在线对比 SGAN，验证其在准确率和运行时上的总体优势。

摘要：

自主移动机器人可以依靠多种人体运动检测与预测系统，在人类环境中实现安全、高效的导航，但其底层模型架构可能对机器人在真实世界中的可信度产生不同影响。在现有的情境感知人体运动预测解决方案中，一些方法已经显示出将符号知识与最先进神经网络相结合的优势。特别是，近期一种神经符号架构 NeuroSyM 已成功利用定性轨迹演算（Qualitative Trajectory Calculus，QTC）嵌入情境信息，以表示空间交互。该工作在离线数据集上取得了优于纯神经基线架构的性能。本文中，我们扩展原始架构，提出 neuROSym：一个用于在真实世界场景中进行机器人部署的 ROS 软件包，能够在线运行、可视化并评估以往用于运动预测的纯神经模型和神经符号模型。我们在一台 TIAGo 机器人上、两种具有不同人体运动模式的场景中评估了这些模型，即 NeuroSyM 和基线 SGAN。我们评估了预测模型的准确率和运行时性能，结果表明，使用我们的神经符号架构时总体性能有所提升。我们将 neuROSym 软件包公开发布给机器人学界。

英文摘要：

Autonomous mobile robots can rely on several human motion detection and prediction systems for safe and efficient navigation in human environments, but the underline model architectures can have different impacts on the trustworthiness of the robot in the real world. Among existing solutions for context-aware human motion prediction, some approaches have shown the benefit of integrating symbolic knowledge with state-of-the-art neural networks. In particular, a recent neuro-symbolic architecture (NeuroSyM) has successfully embedded context with a Qualitative Trajectory Calculus (QTC) for spatial interactions representation. This work achieved better performance than neural-only baseline architectures on offline datasets. In this paper, we extend the original architecture to provide neuROSym, a ROS package for robot deployment in real-world scenarios, which can run, visualise, and evaluate previous neural-only and neuro-symbolic models for motion prediction online. We evaluated these models, NeuroSyM and a baseline SGAN, on a TIAGo robot in two scenarios with different human motion patterns. We assessed accuracy and runtime performance of the prediction models, showing a general improvement in case our neuro-symbolic architecture is used. We make the neuROSym package1 publicly available to the robotics community.

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。

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