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

2026-06-26

AI Summary

This work proposes a cloud-native embodied intelligence simulation framework to address the high cost, poor scalability, and low reproducibility of real-world robotic data collection. The framework employs a four-layer closed-loop architecture that integrates environment generation, task execution, trajectory collection, model evaluation, and unified data management. By leveraging containerized simulation, elastic resource scheduling, and service-oriented design, it standardizes the entire training-evaluation-deployment pipeline. The system supports large-scale concurrent simulation across multiple models and tasks, featuring dynamic scheduling, visual augmentation, and real-time data filtering. These capabilities significantly enhance scalability, automation, and experimental reproducibility, providing an efficient and unified cloud-native infrastructure for embodied intelligence research.

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.

Problem

• embodied intelligence
• simulation environment
• data collection
• scalability
• reproducibility

Innovation

• cloud-native simulation
• embodied intelligence
• scalable infrastructure
• closed-loop data optimization
• containerized simulation