Interactive Robot Knowledge Patching Using Augmented Reality

Liu, Hangxin; Zhang, Yaofang; Si, Wenwen; Xie, Xu; Zhu, Yixin; Zhu, Song‐Chun

doi:10.1109/icra.2018.8462837

Cited by 63 publications

(39 citation statements)

References 14 publications

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“…An experimental setup containing an AR-supported vision system and haptic feedback technology which was used for the remote control of a welding robot is presented in [77]. Liu et al [78] integrated an AR interface with Temporal And-Or Graph (T-AOG) algorithm to provide a robot programmer with information on the robot's hidden states (e.g., latent forces during interaction). In [79], an AR interface was used to program a seven degrees-of-freedom (DoF) manipulator via the method of augmented trajectories.…”

Section: Ar In Robot Control and Planningmentioning

confidence: 99%

Augmented Reality for Robotics: A Review

2020

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Augmented reality (AR) is used to enhance the perception of the real world by integrating virtual objects to an image sequence acquired from various camera technologies. Numerous AR applications in robotics have been developed in recent years. The aim of this paper is to provide an overview of AR research in robotics during the five year period from 2015 to 2019. We classified these works in terms of application areas into four categories: (1) Medical robotics: Robot-Assisted surgery (RAS), prosthetics, rehabilitation, and training systems; (2) Motion planning and control: trajectory generation, robot programming, simulation, and manipulation; (3) Human-robot interaction (HRI): teleoperation, collaborative interfaces, wearable robots, haptic interfaces, brain-computer interfaces (BCIs), and gaming; (4) Multi-agent systems: use of visual feedback to remotely control drones, robot swarms, and robots with shared workspace. Recent developments in AR technology are discussed followed by the challenges met in AR due to issues of camera localization, environment mapping, and registration. We explore AR applications in terms of how AR was integrated and which improvements it introduced to corresponding fields of robotics. In addition, we summarize the major limitations of the presented applications in each category. Finally, we conclude our review with future directions of AR research in robotics. The survey covers over 100 research works published over the last five years.

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Section: Ar In Robot Control and Planningmentioning

confidence: 99%

Augmented Reality for Robotics: A Review

2020

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“…AR/MR interfaces also allow users to intervene in the robot's learning process based on the information displayed. Liu et al proposed an interactive teaching method based on the visualization of the robot's internal representation [50]. MR interfaces have also been introduced for the online teaching of optimal trajectories of robot arms by Ostanin et al [51].…”

Section: Ar and Mr-based Human-robot Interactionsmentioning

confidence: 99%

Semiotically adaptive cognition: toward the realization of remotely-operated service robots for the new normal symbiotic society

et al. 2021

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The installation of remotely-operated service robots in the environments of our daily life (including offices, homes, and hospitals) can improve work-from-home policies and enhance the quality of the so-called new normal. However, it is evident that remotely-operated robots must have partial autonomy and the capability to learn and use local semiotic knowledge. In this paper, we argue that the development of semiotically adaptive cognitive systems is key to the installation of service robotics technologies in our service environments. To achieve this goal, we describe three challenges: the learning of local knowledge, the acceleration of onsite and online learning, and the augmentation of human-robot interactions.

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“…Firstly, robots based on static systems are hard to adapt to user preferences and intuitively interact with users [18,21]. To address this problem and explore human-friendly systems, some recent research enhanced the accuracy of human-robot knowledge communication by acquiring multi-modal human behavior data or the AR interface [25,35,57].…”

Section: Challenges Of Knowledge Graph In Cognitive Robotsmentioning

confidence: 99%