TAMS：面向无线遥机器人操作的任务感知多视角自适应流
TAMS: Task-Aware Multi-View Adaptive Streaming for Wireless Telerobotic Manipulation
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中文总结 AI 辅助
TAMS系统可根据无线遥机器人操作阶段动态分配多视角视频比特率，经6-DoF遥操作测试，其缩短任务时间、提升成功率，性能优于均等及静态分配基线。
中文摘要 AI 辅助
无线遥机器人操作依赖及时的多视角视频反馈，但可用上行带宽往往有限且动态变化。本文提出任务感知多视角自适应流（TAMS）系统，该系统根据当前操作阶段分配视频比特率。TAMS从机器人侧轻量信号推断任务阶段，优先分配与操作者最相关的相机视角，同时保留次要视角的基础可见性。在六自由度（6-DoF）遥操作测试平台上，针对三种受限网络条件开展实验，结果显示：与均等分配和静态分配基线相比，TAMS提升了主视角结构相似性指数（SSIM），缩短了任务完成时间，提高了试验成功率。在最受限带宽条件下，TAMS相较于均等分配将平均完成时间从68.9秒降至43.9秒，试验成功率从48%提升至71%。代码可在该网址获取：this https URL。
英文摘要
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.
发表机构
- Pengcheng Laboratory（鹏城实验室）
- Southern Univ. of Sci. & Tech.（南方科技大学）
机构由 AI 辅助整理，请以论文原文为准。
