AI 中文总结
Arm2Air 实现跨 embodiments 骨架迁移，高效优化无人机中继部署，在规划速度、中继性能及数据效率上均优于基线方法，为异构 embodied 任务结构先验迁移提供新方案。
AI 中文摘要
无人机（UAV）中继网络可在通信基础设施受损后恢复连通性，城市中继部署难度大，需联合考虑视线遮挡、通信范围、高度及三维障碍物。Arm2Air 通过跨 embodiments 迁移，将机器人臂的避障骨架迁移至 UAV 中继部署：预训练 Neural MP 模型生成的源域机器人臂运动被转换为有序骨架，用于预训练基于 Transformer 的迁移平台，再通过有限目标数据和 Low-Rank Adaptation 适配至 UAV 域。迁移后的骨架初始化中继链，针对连通性、瓶颈容量、延迟及移动成本优化。在9个预留的高杂乱三维城市地图上，Arm2Air 较最快的传统规划器将中位数端到端规划运行时间降低64.9%；在另一组30个密集城市地图预留集的高遮挡组中，较 IMPC-MD 提升瓶颈容量32.6%、降低容量方差74.7%、降低最大跳距13.2%、降低跳距方差75.2%、降低中继位移16.9%；仅用3个目标域训练地图时，Arm2Air 较从头训练将中继位置均方根误差降低53.6%，且仅更新0.134百万参数，而从头训练和全微调需更新1.383百万参数。这些结果证明了计算和数据高效的 UAV 中继部署，并为跨异构 embodied 任务迁移有序结构先验提供了更广泛的原则。
英文摘要
Unmanned aerial vehicle (UAV) relay networks can restore connectivity after communication infrastructure is damaged. Urban relay placement is difficult because line-of-sight blockage, communication range, altitude, and three-dimensional obstacles must be considered jointly. Arm2Air transfers obstacle-avoidance skeletons from robot arms to UAV relay placement through cross-embodiment transfer. Source-domain robot-arm motions from a pretrained Neural MP model are converted into ordered skeletons that pretrain a transformer-based transfer platform, which is then adapted to the UAV domain using limited target data and Low-Rank Adaptation. The transferred skeleton initializes a relay chain that is refined for connectivity, bottleneck capacity, delay, and movement cost. On nine held-out high-clutter 3D urban maps, Arm2Air reduced median end-to-end planning runtime by 64.9 percent relative to the fastest conventional planner. On the high-obstruction group of a separate 30-map dense urban holdout, it increased bottleneck capacity by 32.6 percent, reduced capacity variance by 74.7 percent, reduced maximum hop distance by 13.2 percent, reduced hop-distance variance by 75.2 percent, and reduced relay displacement by 16.9 percent relative to IMPC-MD. With only three target-domain training maps, Arm2Air reduced relay-position root mean square error by 53.6 percent relative to training from scratch while updating 0.134 million parameters, compared with 1.383 million for Scratch and Full Fine-tuning. These results demonstrate computationally and data-efficient UAV relay placement and suggest a broader principle for transferring ordered structural priors across heterogeneous embodied tasks.
Comments9 pages, 4 figures
