Mobile robotic manipulation--the ability of robots to navigate spaces and interact with objects--is a core capability of physical AI. Foundation models have led to breakthroughs in their performance, but at a significant computational cost. We present the first measurement study of mobile robotic manipulation workloads across onboard, edge, and cloud GPU platforms. We find that the full workload stack is infeasible to run on smaller onboard GPUs, while larger onboard GPUs drain robot batteries several hours faster. Offloading alleviates these constraints but introduces its own challenges, as additional network latency degrades task accuracy, and the bandwidth requirement makes naive cloud offloading impractical. Finally, we quantify opportunities and pitfalls of sharing compute across robot fleets. We believe our measurement study will be crucial to designing inference systems for mobile robots.

翻译：移动机器人操控——即机器人自主导航并与物体交互的能力——是物理人工智能的核心能力。基础模型虽显著提升了其性能，但带来了巨大的计算开销。我们首次对移动机器人操控工作负载在机载、边缘和云端GPU平台上进行了测量研究。研究发现：完整的工作负载栈无法在小型机载GPU上运行，而大型机载GPU会使机器人电池续航时间缩短数小时。任务卸载虽缓解了这些限制，却引入了新挑战——额外的网络延迟会降低任务精度，带宽需求更使得朴素云端卸载不切实际。最后，我们量化了机器人集群间共享计算资源的机遇与陷阱。我们认为，此项测量研究对设计移动机器人推理系统至关重要。