EmbodiedIntentBench：面向移动机器人任务意图驱动运行时适配的基准
EmbodiedIntentBench: Benchmarking Intent-Driven Runtime Adaptation for Mobile Robotic Missions
做无人机或机器人自主系统的开发者可以看看这个新基准。它把任务意图拆成五类结构化契约，测试发现召回率 87.5% 的配置 16 次任务只完成 1 次，比它召回率低的反而完成 14 次，还会用 Jetson AGX Orin 的配置重放能耗，能避免你在错误指标上做策略选型。
arXiv 上发布的 EmbodiedIntentBench 是一个针对移动机器人任务的基准，评估运行时决策是否真正达成任务目标，而非只看孤立的感知精度或执行效率。基准用结构化契约表示任务意图，覆盖覆盖范围、时间性证据、上报、紧迫度和资源五个维度，并将模拟无人机（UAV）工作负载与因果化、计入资源消耗的执行耦合起来，使计算决策会实际影响任务时间、资源使用和哪些观测能算作证据。
在多阶段 Depot 研究中，一个对可见目标观测达到 87.5% 全库目标召回率的配置，16 次任务只完成 1 次，而召回率更低的配置完成了 14 次。消融实验显示，忽略执行导致的观测损失会推高总体任务成功率，甚至反转两个运行时策略的排序。用 Jetson AGX Orin 的 CPU 和 GPU 配置重放时，本地与远程策略的相对成败也会改变。在部分 Jetson GPU 配置重放中，意图驱动适配在任务成功率不变的情况下，将建模任务能耗降低最多 28%。
对从事无人机自主系统、边缘计算部署的团队来说，这个基准说明单看召回率等感知指标可能严重误导策略选型，需要在物理与资源约束下按任务完成度来评估运行时决策。
EmbodiedIntentBench: Benchmarking Intent-Driven Runtime Adaptation for Mobile Robotic Missions
Mobile robotic sensing systems must adapt to operator requirements, limited resources, changing connectivity, and observation opportunities shaped by physical motion. Evaluating runtime strategies therefore requires assessing whether their decisions fulfill mission objectives, beyond isolated perception accuracy or execution efficiency. We present EmbodiedIntentBench, a benchmark for intent-driven runtime adaptation in mobile robotic missions. It represents mission intent through structured contracts for coverage, temporal evidence, reporting, urgency, and resources, and couples simulated unmanned aerial vehicle (UAV) workloads with causal, resource-accounted execution. Computation decisions affect mission time, resource use, and which observations can contribute evidence. We evaluate fixed and adaptive perception strategies in multi-phase missions and under changing urgency, connectivity, and battery conditions. In the multi-phase Depot study, a configuration with 87.5% full-bank target recall across visible target observations completes only 1 of 16 missions, whereas a lower-recall configuration completes 14. In an ablation, ignoring execution-induced observation loss increases aggregate mission success and reverses the ordering of two runtime strategies. Replays using Jetson AGX Orin CPU and GPU profiles change the relative success of local and remote strategies. In selected Jetson GPU-profile replays, intent-driven adaptation reduces modeled task energy by up to 28% with unchanged mission success. EmbodiedIntentBench provides an extensible framework for evaluating runtime decisions against mission fulfillment under physical and resource constraints.
本条情报归属 AITOP「论文」核心追踪栏目。在跨区域产业观察坐标中，该动态直接关联「AI 开发者工具链、Coding Agent 与工程化实践」研究主线，反映出当前技术演进与商业落地的最新风向。涉及核心实体与技术议题包括：EmbodiedIntentBench、无人机、Jetson、边缘计算，读者可通过下方标签进入对应专题页查阅历史脉络与关联对比。
信源核验说明：本文原始线索采编自「arXiv cs.LG」（@Rajat Bhattacharjya, Changwoon Lee, Hyeonjong Ju, Sing-Yao Wu, Hamidreza Alikhani, Zhuoyi Yang, Ian Harris, Nalini Venkatasubramanian, Nikil Dutt），经 AITOP 多维评分管线结构化提炼。