{"ID":5551587,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-04T15:45:26.395003369Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.01088","arxiv_id":"2607.01088","title":"ROSA: A Robotics Foundation Model Serving System for Robot Factories","abstract":"Robotics foundation models (RFMs) are making general-purpose robots increasingly practical for factory deployments. While RFM serving systems are central to this vision, existing systems are largely shaped by a single-robot, single-model assumption: inference is treated as an edge-computing problem handled by an on-robot or dedicated nearby GPU, and the serving objective is to minimize the latency of a single action model. In this paper, we propose ROSA, an RFM serving system for robot factories designed around three key principles. First, ROSA adopts shared GPU-pool serving, allowing a fleet of robots to access powerful server-class GPUs over the network in order to improve inference performance, battery duration, and GPU utilization. Second, ROSA provides a robotics-aware programming abstraction and system design that supports multi-model pipelines, per-task performance requirements, and failure handling. Third, ROSA uses factory-objective-driven scheduling to maximize SLO-qualified factory productivity rather than minimizing individual request latency. We implement ROSA on top of Ray Serve for distributed orchestration, with vLLM, PyTorch, and JAX as model-serving backends, and evaluate it on both real robots and synthetic large-scale workloads. The results show that ROSA improves factory productivity by up to 12.06x over conventional dedicated serving systems.","short_abstract":"Robotics foundation models (RFMs) are making general-purpose robots increasingly practical for factory deployments. While RFM serving systems are central to this vision, existing systems are largely shaped by a single-robot, single-model assumption: inference is treated as an edge-computing problem handled by an on-rob...","url_abs":"https://arxiv.org/abs/2607.01088","url_pdf":"https://arxiv.org/pdf/2607.01088v1","authors":"[\"Wenqi Jiang\",\"Jason Clemons\",\"Rowland O'Flaherty\",\"Hugo Hadfield\",\"Alperen Degirmenci\",\"Shuran Song\",\"Yashraj Narang\",\"Christos Kozyrakis\"]","published":"2026-07-01T15:45:08Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.DC\"]","methods":"[\"Large Language Model\"]","has_code":false}