Are You Still With Me? Continuous Engagement Assessment From a Robot's Point of View.
Are You Still With Me? Continuous Engagement Assessment From a Robot's Point of View.
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DOI：
10.3389/frobt.2020.00116
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发表时间：
2020
影响因子：
3.4
通讯作者：
Hanheide M
中科院分区：
文献类型：
--
作者：
Del Duchetto F;Baxter P;Hanheide M
在人机交互（HRI）环境中持续测量用户与机器人的参与度，为原位强化学习铺平了道路，提高了交互质量的指标，并可以指导交互设计和行为优化。然而，用户粘性通常被认为是非常多方面的，很难用一个可行的通用计算模型来捕捉，这个模型可以作为用户粘性的总体衡量标准。基于人类在看到某种程度的参与时成功评估情况的直观方式，我们提出了一种新的回归模型（利用CNN和LSTM网络），使机器人能够在与人类互动期间从标准视频流中计算单个标量参与，从交互机器人的角度获得。该模型基于部署在公共博物馆的自动导游机器人的长期数据集，并由三名独立编码员对数字参与评估进行连续注释。我们展示了这个模型不仅可以很好地预测我们自己的应用领域的参与度，而且还展示了它成功地转移到一个完全不同的数据集（不同的任务、环境、相机、机器人和人）。经过训练的模型和软件可在人力资源研究所社区（https://github.com/LCAS/engagement_detector）获得，作为衡量各种情况下参与情况的工具。
Continuously measuring the engagement of users with a robot in a Human-Robot Interaction (HRI) setting paves the way toward in-situ reinforcement learning, improve metrics of interaction quality, and can guide interaction design and behavior optimization. However, engagement is often considered very multi-faceted and difficult to capture in a workable and generic computational model that can serve as an overall measure of engagement. Building upon the intuitive ways humans successfully can assess situation for a degree of engagement when they see it, we propose a novel regression model (utilizing CNN and LSTM networks) enabling robots to compute a single scalar engagement during interactions with humans from standard video streams, obtained from the point of view of an interacting robot. The model is based on a long-term dataset from an autonomous tour guide robot deployed in a public museum, with continuous annotation of a numeric engagement assessment by three independent coders. We show that this model not only can predict engagement very well in our own application domain but show its successful transfer to an entirely different dataset (with different tasks, environment, camera, robot and people). The trained model and the software is available to the HRI community, at https://github.com/LCAS/engagement_detector, as a tool to measure engagement in a variety of settings.