Toet Alexander, Kuling Irene A, Krom Bouke N, van Erp Jan B F
Perceptual and Cognitive Systems, Netherlands Organisation for Applied Scientific Research (TNO), Soesterberg, Netherlands.
Intelligent Autonomous Systems, Netherlands Organisation for Applied Scientific Research (TNO), The Hague, Netherlands.
Front Robot AI. 2020 Feb 11;7:14. doi: 10.3389/frobt.2020.00014. eCollection 2020.
Telerobotics aims to transfer human manipulation skills and dexterity over an arbitrary distance and at an arbitrary scale to a remote workplace. A telerobotic system that is transparent enables a natural and intuitive interaction. We postulate that embodiment (with three sub-components: sense of ownership, agency, and self-location) of the robotic system leads to optimal perceptual transparency and increases task performance. However, this has not yet been investigated directly. We reason along four premises and present findings from the literature that substantiate each of them: (1) the brain can embody non-bodily objects (e.g., robotic hands), (2) embodiment can be elicited with mediated sensorimotor interaction, (3) embodiment is robust against inconsistencies between the robotic system and the operator's body, and (4) embodiment positively correlates to dexterous task performance. We use the predictive encoding theory as a framework to interpret and discuss the results reported in the literature. Numerous previous studies have shown that it is possible to induce embodiment over a wide range of virtual and real extracorporeal objects (including artificial limbs, avatars, and android robots) through mediated sensorimotor interaction. Also, embodiment can occur for non-human morphologies including for elongated arms and a tail. In accordance with the predictive encoding theory, none of the sensory modalities is critical in establishing ownership, and discrepancies in multisensory signals do not necessarily lead to loss of embodiment. However, large discrepancies in terms of multisensory synchrony or visual likeness can prohibit embodiment from occurring. The literature provides less extensive support for the link between embodiment and (dexterous) task performance. However, data gathered with prosthetic hands do indicate a positive correlation. We conclude that all four premises are supported by direct or indirect evidence in the literature, suggesting that embodiment of a remote manipulator may improve dexterous performance in telerobotics. This warrants further implementation testing of embodiment in telerobotics. We formulate a first set of guidelines to apply embodiment in telerobotics and identify some important research topics.
远程机器人技术旨在将人类的操作技能和灵活性在任意距离和任意规模上传递到远程工作场所。一个具有透明度的远程机器人系统能够实现自然且直观的交互。我们推测,机器人系统的具身化(包括三个子组件:所有权感、能动性和自我定位)会带来最佳的感知透明度并提高任务表现。然而,这一点尚未得到直接研究。我们基于四个前提进行推理,并展示文献中的研究结果来证实每一个前提:(1)大脑能够将非身体的物体(如机器人手)具身化,(2)具身化可以通过介导的感觉运动交互来引发,(3)具身化对于机器人系统与操作者身体之间的不一致具有鲁棒性,(4)具身化与灵巧的任务表现呈正相关。我们使用预测编码理论作为框架来解释和讨论文献中报道的结果。此前众多研究表明,通过介导的感觉运动交互,在广泛的虚拟和真实体外物体(包括假肢、化身和仿人机器人)上诱导具身化是可能的。此外,具身化也可能发生在非人类形态上,包括长臂和尾巴。根据预测编码理论,在建立所有权方面,没有一种感觉模态是至关重要的,多感觉信号中的差异不一定会导致具身化的丧失。然而,在多感觉同步或视觉相似度方面的巨大差异可能会阻止具身化的发生。文献对具身化与(灵巧的)任务表现之间的联系提供的支持较少。然而,使用假肢手收集的数据确实表明存在正相关。我们得出结论,文献中的直接或间接证据支持了所有这四个前提,这表明远程操纵器的具身化可能会提高远程机器人技术中的灵巧性能。这需要在远程机器人技术中对具身化进行进一步的实施测试。我们制定了第一套在远程机器人技术中应用具身化的指导方针,并确定了一些重要的研究课题。