根据操作阶段动态分配视频码率，提升远程操控效率
TAMS: Task-Aware Multi-View Adaptive Streaming for Wireless Telerobotic Manipulation
- 根据机器人侧轻量信号判断任务阶段，动态调整视频码率
- 在最严苛带宽下任务完成时间缩短至43.9秒，成功率从48%提至71%
- 适合对实时性要求高的无线遥操作场景
无线遥操作依赖及时的多视角视频反馈，但上行带宽常受限且动态变化。本文提出任务感知多视角自适应流传输系统（TAMS），根据当前操作阶段分配视频码率。TAMS通过轻量级机器人侧信号推断任务阶段，优先保障对操作者最相关的摄像头视角质量，同时保持次要视角的基础可视性。在六自由度（6-DoF）遥操作测试平台上，三种受限网络条件下实验表明，相较于均等与静态分配基线，TAMS提升了主视角结构相似性指数（SSIM），缩短了任务完成时间，并提高了试验成功率。在最严苛带宽条件下，平均完成时间从68.9秒降至43.9秒，成功率从48%提升至71%。代码已开源：https://github.com/Dzxx623/TAMS。
原文摘要 · Abstract (English)
Wireless telerobotic manipulation relies on timely multi-view video feedback, but the available uplink bandwidth is often limited and dynamic. This paper presents Task-Aware Multi-View Adaptive Streaming (TAMS), a system that allocates video bitrate according to the current manipulation phase. TAMS infers task phase from lightweight robot-side signals and prioritizes the camera view most relevant to the operator while preserving baseline visibility for secondary views. Experiments on a six-degree-of-freedom (6-DoF) teleoperation testbed under three constrained network conditions show that TAMS improves primary view Structural Similarity Index (SSIM), reduces task completion time, and increases trial success rate compared with equal and static allocation baselines. Under the most constrained bandwidth condition, TAMS reduces mean completion time from 68.9 s to 43.9 s relative to equal allocation and increases trial success rate from 48% to 71%. Code is available at: https://github.com/Dzxx623/TAMS.
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