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.

自主移动机器人可以在人类环境中依赖多种人体运动检测和预测系统，但底层模型架构可能对机器人在现实世界中的可信度产生不同的影响。本文针对基于上下文的人体运动预测的现有解决方案中的一些方法，展示了将符号知识与现代神经网络相结合的好处。尤其是最近的神经符号结构（NeuroSyM）成功地将上下文与定性轨迹计算（QTC）相结合，以进行空间交互表示。这项工作在离线数据集上比仅使用神经网络的基准架构取得了更好的性能。为了在实际场景中为机器人部署提供便利，我们将原始架构扩展为 neuROSym，一个面向机器人部署的 ROS 软件包，可以在线运行、可视化和评估以往的仅使用神经网络和神经符号模型进行运动预测的模型。我们在两个具有不同人体运动模式的场景中评估了这些模型，NeuroSyM 和基准 SGAN，并评估了预测模型的准确性和运行时性能，结果表明使用我们的神经符号架构普遍改善了性能。我们将 neuROSym 软件包公开提供给机器人社区。

neuROSym：基于 ROS 的神经符号模型在人体运动预测中的部署和评估

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

Context: Software engineering has a problem in that when we empirically
evaluate competing prediction systems we obtain conflicting results. Objective:
To reduce the inconsistency amongst validation study results and provide a more
formal foundation to interpret results with a particular focus on continuous
prediction systems. Method: A new framework is proposed for evaluating
competing prediction systems based upon (1) an unbiased statistic, Standardised
Accuracy, (2) testing the result likelihood relative to the baseline technique
of random 'predictions', that is guessing, and (3) calculation of effect sizes.
Results: Previously published empirical evaluations of prediction systems are
re-examined and the original conclusions shown to be unsafe. Additionally, even
the strongest results are shown to have no more than a medium effect size
relative to random guessing. Conclusions: Biased accuracy statistics such as
MMRE are deprecated. By contrast this new empirical validation framework leads
to meaningful results. Such steps will assist in performing future
meta-analyses and in providing more robust and usable recommendations to
practitioners.

提出一个新的框架来进行预测系统的评估，并检验了现有的统计指标的有效性，呈现出可靠的实验结论。