Building a Scalable, Reproducible, Evaluatable, and Closed-Loop Simulation Environment Foundation for Embodied Intelligence Cloud-Native Simulation Infrastructure for Embodied Intelligence Training, Evaluation, and Data Collection

Published June 29, 2026 · Category: Robotics

Overview

arXiv:2606.27962v1 Announce Type: new Abstract: This paper presents a cloud-native simulation infrastructure framework for embodied intelligence that supports large-scale training, standardized evaluation, and simulation-based data collection. The framework unifies simulation environment generation, task execution, trajectory collection, model evaluation, data management, and cloud services into a scalable and reproducible platform. To address the high cost, limited scalability, and poor reproducibility of real-world robotic data collection, the framework adopts cloud-native technologies including elastic resource scheduling, containerized simulation, unified data management, and service-oriented system design, enabling efficient large-scale simulation for multi-model and multi-task workloads. Built on a four-layer architecture, the framework provides standardized environment assets, automated task generation, trajectory collection, benchmark evaluation, and closed-loop data optimization. It further integrates representative systems including D-VLA, RL-VLA3, Sword, and Pre-VLA to support scalable simulation, dynamic scheduling, visual augmentation, and real-time data filtering. We argue that cloud-native simulation infrastructure provides a unified foundation for data generation, model training, standardized evaluation, and real-world deployment, and will play a key role in the future development of embodied intelligence.

Source

Originally published at arxiv.org.

Related Articles

• SimFoundry: Modular and Automated Scene Generation for Policy Learning and Evaluation
• DexCompose: Reusing Dexterous Policies for Multi-Task Manipulation with a Single Hand
• Swarm sign language: motion-based communication between drones