A Generative Model-Based Predictive Display for Robotic Teleoperation

Bowen Xie, Mingjie Han, Jun Jin, Martin Barczyk, Martin Jägersand
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Abstract

We propose a new generative model-based predictive display for robotic teleoperation over high-latency communication links. Our method is capable of rendering photo-realistic images of the scene to the human operator in real time from RGB-D images acquired by the remote robot. A preliminary exploration stage is used to build a coarse 3D map of the remote environment and to train a generative model, both of which are then used to generate photo-realistic images for the human operator based on the commanded pose of the robot. Data captured by the remote robot is used to dynamically update the 3D map, enabling teleoperation in the presence of new and relocated objects. Various experiments validate our proposed method’s performance and benefits over alternative methods.
基于生成模型的机器人遥操作预测显示
我们提出了一种新的基于生成模型的预测显示,用于机器人在高延迟通信链路上的远程操作。我们的方法能够将远程机器人获取的RGB-D图像实时呈现给人类操作员。初步探索阶段用于建立远程环境的粗略3D地图和训练生成模型,然后根据机器人的命令姿势为人类操作员生成逼真的图像。远程机器人捕获的数据用于动态更新3D地图,在新的和重新定位的物体存在时实现远程操作。各种实验验证了我们提出的方法的性能和优于其他方法的优点。
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