Learning Important Regions via Attention for Video Streaming on Cloud Robotics

Abstract

Cloud robotics, i.e., controlling robots from the cloud, make it possible to perform more complex processes, make robots smaller, and coordinate multi-robots by sharing information between robots and utilizing abundant computing resources. In cloud robotics, robots need to transmit videos to the cloud in real time to recognize their surroundings. Lowering the video quality reduces the bitrate in low bandwidth environments; however, this may lead to control errors and misrecognition due to lack of detailed image features. Even with 5G, bandwidth fluctuates widely, especially in moving robots, making it difficult to upload high quality video consistently. To reduce bitrate while preserving Quality of Control (QoC), we propose a method of learning the important regions for a pretrained autonomous agent using self-attention, and transmitting the video to the agent by controlling the image quality of each region on the basis of the estimated importance. The evaluation results demonstrate that our approach can maintain QoC while reducing the bitrate to 26% by setting important regions to high quality and the rest to low quality.