A service assistant combining autonomous robotics, flexible goal formulation, and deep-learning-based brain-computer interfacing
A service assistant combining autonomous robotics, flexible goal formulation, and deep-learning-based brain-computer interfacing
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DOI：
10.1016/j.robot.2019.02.015
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发表时间：
2019-06-01
影响因子：
4.3
通讯作者：
Burgard, W.
中科院分区：
文献类型：
--
作者：
Kuhner, D.;Fiederer, L. D. J.;Burgard, W.
随着自主服务机器人变得越来越便宜，从而为公众所用，越来越需要用户友好的界面来控制这些系统。随着机器人任务和环境的复杂性增加，控制界面通常变得更加复杂。传统的控制方式，如触摸，语音或手势，不一定适合所有用户。虽然一些用户可以努力使自己熟悉机器人系统，但是具有运动障碍的用户可能无法控制这样的系统，即使他们最需要机器人辅助。在本文中，我们提出了一个新的框架，允许这些用户与机器人服务助理在一个闭环的方式，只使用思想进行交互。该系统由几个交互组件组成：脑机接口（BCI），使用非侵入性神经元信号记录和协同自适应深度学习，基于参考表达式的高级任务规划，导航和操纵规划以及环境感知。我们广泛评估的BCI在各种任务，确定目标制定用户界面的性能，并在用户研究中调查其直观性。此外，我们证明了该系统在真实的世界场景中的适用性和鲁棒性，考虑取和携带任务，密切的人机交互和存在意想不到的变化。正如我们的研究结果所示，该系统能够适应环境的频繁变化，并在合理的时间内可靠地完成给定的任务。结合高层次的任务规划的基础上引用表达式和自主机器人系统，有趣的新的视角打开了非侵入性BCI为基础的人机交互。(C)2019年，任作家。由爱思唯尔公司出版
As autonomous service robots become more affordable and thus available for the general public, there is a growing need for user-friendly interfaces to control these systems. Control interfaces typically get more complicated with increasing complexity of robotic tasks and environments. Traditional control modalities such as touch, speech or gesture are not necessarily suited for all users. While some users can make the effort to familiarize themselves with a robotic system, users with motor disabilities may not be capable of controlling such systems even though they need robotic assistance most. In this paper, we present a novel framework that allows these users to interact with a robotic service assistant in a closed-loop fashion, using only thoughts. The system is composed of several interacting components: a brain-computer interface (BCI) that uses non-invasive neuronal signal recording and co-adaptive deep learning, high-level task planning based on referring expressions, navigation and manipulation planning as well as environmental perception. We extensively evaluate the BCI in various tasks, determine the performance of the goal formulation user interface and investigate its intuitiveness in a user study. Furthermore, we demonstrate the applicability and robustness of the system in real world scenarios, considering fetch-and-carry tasks, close human-robot interactions and in presence of unexpected changes. As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time. Combined with high-level task planning based on referring expressions and an autonomous robotic system, interesting new perspectives open up for non-invasive BCI-based human-robot interactions. (C) 2019 The Authors. Published by Elsevier B.V.