Teaching System for Multimodal Object Categorization by Human-Robot Interaction in Mixed Reality
Teaching System for Multimodal Object Categorization by Human-Robot Interaction in Mixed Reality
复制标题
混合现实中人机交互多模态物体分类教学系统
DOI：
10.1109/ieeeconf49454.2021.9382607
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Taniguchi Tadahiro
中科院分区：
文献类型：
--
作者：
Hafi Lotfi El;Nakamura Hitoshi;Taniguchi Akira;Hagiwara Yoshinobu;Taniguchi Tadahiro
随着服务机器人对支持老龄化社会变得至关重要，教它们如何执行一般服务任务仍然是阻止它们在日常生活环境中部署的主要挑战。此外，为一般服务任务开发人工智能需要自下而上的无监督方法，让机器人从自己的观察和与用户的互动中学习。然而，与自上而下的监督方法（如深度学习）相比，学习的程度与提供给机器人的预先存在的数据的数量和种类直接相关，因此从人类的角度来看相对容易理解，自下而上方法中的学习状态本质上更难理解和可视化。为了解决这些问题，我们提出了一个多模态对象分类的教学系统，人机交互通过混合现实（MR）可视化。特别是，我们提出的系统使用户能够监测和干预机器人的对象分类过程的基础上多模态潜在狄利克雷分配（MLDA），以解决意外的结果，并加快学习。我们的贡献是双重的：1）描述了一个服务机器人，MR交互和MLDA对象分类在一个统一的系统中的集成，以及2）提出了一个MR用户界面，通过直观的可视化和交互来教机器人。
As service robots are becoming essential to support aging societies, teaching them how to perform general service tasks is still a major challenge preventing their deployment in daily-life environments. In addition, developing an artificial intelligence for general service tasks requires bottom-up, unsupervised approaches to let the robots learn from their own observations and interactions with the users. However, compared to the top-down, supervised approaches such as deep learning where the extent of the learning is directly related to the amount and variety of the pre-existing data provided to the robots, and thus relatively easy to understand from a human perspective, the learning status in bottom-up approaches is by their nature much harder to appreciate and visualize. To address these issues, we propose a teaching system for multimodal object categorization by human-robot interaction through Mixed Reality (MR) visualization. In particular, our proposed system enables a user to monitor and intervene in the robot’s object categorization process based on Multimodal Latent Dirichlet Allocation (MLDA) to solve unexpected results and accelerate the learning. Our contribution is twofold by 1) describing the integration of a service robot, MR interactions, and MLDA object categorization in a unified system, and 2) proposing an MR user interface to teach robots through intuitive visualization and interactions.
DOI：
10.1007/978-1-4842-5845-3_6
发表时间：
2020
期刊：
Immersive Office 365
影响因子：
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作者：
A.W.M. Meijers
通讯作者：
A.W.M. Meijers
DOI：
10.1109/sii.2017.8279367
发表时间：
2017
期刊：
2017 IEEE/SICE International Symposium on System Integration (SII)
影响因子：
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作者：
K. Wada
通讯作者：
K. Wada
使用增强现实进行交互式机器人知识修补
DOI：
10.1109/icra.2018.8462837
发表时间：
2018
期刊：
2018 IEEE International Conference on Robotics and Automation (ICRA)
影响因子：
--
作者：
Hangxin Liu;Yaofang Zhang;Wenwen Si;Xu Xie;Yixin Zhu;Song
通讯作者：
Song