我们从两个在线人机互动实验中介绍了数据，其中227名参与者观看了人形机器人的视频，表现出有缺陷或非故障行为，同时保持沉默或说话。要求参与者评估他们对机器人的信任度的看法，以及其可爱，动画和感知的情报。结果表明，虽然一个非故障机器人达到了最高的信任，但可以说出来的似乎有故障的机器人可以完全减轻否则会出现错误行为的信任损失。我们认为，这种缓解与在存在语音时也可以看到的感知智能的增加有关。
translated by 谷歌翻译
We consider the problem of embodied visual navigation given an image-goal (ImageNav) where an agent is initialized in an unfamiliar environment and tasked with navigating to a location 'described' by an image. Unlike related navigation tasks, ImageNav does not have a standardized task definition which makes comparison across methods difficult. Further, existing formulations have two problematic properties; (1) image-goals are sampled from random locations which can lead to ambiguity (e.g., looking at walls), and (2) image-goals match the camera specification and embodiment of the agent; this rigidity is limiting when considering user-driven downstream applications. We present the Instance-specific ImageNav task (InstanceImageNav) to address these limitations. Specifically, the goal image is 'focused' on some particular object instance in the scene and is taken with camera parameters independent of the agent. We instantiate InstanceImageNav in the Habitat Simulator using scenes from the Habitat-Matterport3D dataset (HM3D) and release a standardized benchmark to measure community progress.
translated by 谷歌翻译
We present a retrospective on the state of Embodied AI research. Our analysis focuses on 13 challenges presented at the Embodied AI Workshop at CVPR. These challenges are grouped into three themes: (1) visual navigation, (2) rearrangement, and (3) embodied vision-and-language. We discuss the dominant datasets within each theme, evaluation metrics for the challenges, and the performance of state-of-the-art models. We highlight commonalities between top approaches to the challenges and identify potential future directions for Embodied AI research.
translated by 谷歌翻译
网络物理系统（CPS）的复杂性日益增加，使工业自动化具有挑战性。需要处理大量传感器记录的数据，以充分执行诸如故障的诊断之类的任务。解决这种复杂性的一种有希望的方法是因果关系的概念。但是，大多数有关因果关系的研究都集中在推断未知系统部分之间的因果关系。工程以根本不同的方式使用因果关系：复杂的系统是通过将组件与已知可控行为相结合的。由于CP是通过第二种方法构建的，因此大多数基于数据的因果模型不适合工业自动化。为了弥合这一差距，提出了针对工业自动化各种应用程序领域的统一因果模型，这将允许更好地沟通和跨学科的更好的数据使用。最终的模型在数学上描述了CPS的行为，并且由于对应用领域的独特要求评估了该模型，因此证明统一的因果关系模型可以作为在工业自动化中应用新方法的基础，该方法侧重于机器学习。
translated by 谷歌翻译