🛰️ Daily AI Frontier
‹ back to 2026-09-24

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

Industry & News Embodied AI Systems

Ranking

Overall 71
Content 80
Popularity 50

Observed public metrics from 1 member.

Representative image for 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

Merged summary

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.

Sources (1)

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

量子位 量子位的朋友们 2026-09-24 arXiv:2602.07837
Public signals Hugging Face upvotes 57
Providers: Hugging Face · Upvotes 57 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-09-25 14:16:03.908616 UTC

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.
item →