5分钟完成机器人纳管、10秒启动跨集群任务，清华大学联合无问芯穹开源具身智能云原生平台RLark

TL;DR - Tsinghua University and Infinigence AI have open-sourced RLark, a cloud-native platform for orchestrating robots, sensors, compute, and software across distributed clusters. It aims to make large-scale embodied-AI experiments easier to deploy, network, monitor, and reuse.

• RLark abstracts robots and cameras as schedulable Kubernetes resources alongside GPUs, reducing device onboarding from about one hour to five minutes in testing.
• Declarative Job/Task/Worker configurations deploy training, inference, and robot-interaction components across clusters; prepared tasks reportedly start within 10 seconds.
• A Beijing–Guangdong test with RLinf completed a 36-minute data-collection, cloud-training, and real-robot validation loop spanning 323 training steps.
• Task-isolated networking uses virtual addressing, gVisor, and SSH tunnels; tests reported up to 49% higher large-packet throughput than the compared VPN and nearly 2 Gbps under high-bandwidth conditions.