← Back to all news
arXiv Machine Learning By Dionysios Adamopoulos, Nattapol Chanpaisit, Basel Fakhri, Christina Giannoula

Efficient Vision-Language-Action Management and Serving for Robot Factories

Read the original on arXiv Machine Learning →

Robion is a new serving and management system designed to run Vision‑Language‑Action (VLA) models on multi‑GPU edge servers for robot factories. It splits the VLM and ADiT stages within a single GPU, shares streams across multiple models, and prioritizes requests by remaining SLO time, enabling high robot load while meeting strict latency requirements. In experiments, Robion achieves 6.7× higher robot load than vLLM‑Omni and 1.5× higher than a monolithic pipeline, and can serve 64 robots on a 4‑GPU server with 98% SLO attainment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Aug 18

Efficient Block-Layer Parallel Inference for Vision-Language-Action on Hybrid Architectures

arXiv:2608. 14586v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models are becoming a promising paradigm for autonomous driving, but their deployment on existing vehicle platforms remains difficult because they introduce both high inference latency and strong GPU-side resource pressure.

By Haibo HU, Lianming Huang, Qiao Li, Nan Guan, Chun Jason Xue
llmsagentsroboticsefficiencymultimodal
More like this →
arXiv Machine Learning
Jun 8

RhinoVLA Technical Report

arXiv:2606. 07383v1 Announce Type: cross Abstract: Vision-Language-Action (VLA) models have shown strong potential for robotic manipulation, but real-time deployment on edge hardware remains challenging.

By Huixi Intelligence, :, Chen Zhang, Chenyang Zhou, Guanglei Ding, Guanghui He, Haibin Gao, Jiajia Chen, Jianyong Zhang, Lianyi Yu, Ningyi Xu, Ping Xu, Qingchen Li, Yingjun Hu, Yijia Zhang, Yuxi Liu
roboticsfine-tuningmultimodal
More like this →
arXiv AI
Jul 20

JoyNexus: Service-Oriented Multi-Tenant Post-Training for VLA Models

arXiv:2607. 16074v1 Announce Type: cross Abstract: The post-training of Vision-Language-Action (VLA) models is essential due to the diversity of simulators, robot embodiments, and task objectives.

By Haoran Sun, Wentao Zhang, Junyang Hua, Hedan Yang, Yongjian Guo, Yifei Zhang, Xiaolong Xiang, Mingxi Luo, Jing Long, Chen Zhao, Chen Zhou, Wanting Xu, Qiming Yang, Hui Zhang, Song Wang, Xiaodong Bai, Shuai Di, Xu Chu, Xiaotie Deng, Yicheng Gong, Junwu Xiong
reinforcement-learningroboticsfine-tuningmultimodal
More like this →
arXiv AI
Aug 5

PhyAI: Real-Time Physical AI at the Edge, Scalable Rollouts in the Cloud

arXiv:2608. 03682v1 Announce Type: new Abstract: Physical AI policies require inference throughout their lifecycle, including model evaluation, cloud reinforcement learning rollout, edge GPU serving, and onboard deployment.

By Chenghua Wang, Daliang Xu, Dongqi Cai, Duojin Sun, Hao Zhang, Haoze Qian, Huaiyuan Zhang, Jinshuo Cui, Kezhao Zhao, Longxi Gao, Mengwei Xu, Rongjie Yi, Tianyue Zhang, Weikai Xie, Xiyuan Tan, Xuanzhe Liu, Yingying Qin, Yiwen Lu, Yuan Yao, Yuezhi Zu, Yunhan Guo, Ziqi Guo
reinforcement-learningroboticsefficiencymultimodalbenchmarks
More like this →
arXiv AI
Jun 16

RollArt: Disaggregated Multi-Task Agentic RL Training at Scale

arXiv:2512. 22560v2 Announce Type: replace-cross Abstract: Agentic Reinforcement Learning (RL) trains LLMs through multi-turn interactions with environments, producing workloads that mix compute-bound prefill, bandwidth-bound decoding, CPU-heavy environment execution, and bursty reward evaluation.

By Wei Gao, Yuheng Zhao, Tianyuan Wu, Shaopan Xiong, Weixun Wang, Dakai An, Lunxi Cao, Dilxat Muhtar, Zichen Liu, Haizhou Zhao, Ju Huang, Siran Yang, Yongbin Li, Wenbo Su, Jiamang Wang, Lin Qu, Bo Zheng, Wei Wang
llmsagentsreinforcement-learningefficiency
More like this →