移动操作是具身智能机器人的一项基础能力。在非结构化家庭环境中实现鲁棒且泛化性强的操作能力，正推动具身智能平台的快速发展。然而，实现无缝的实-仿-实迁移仍面临三大关键挑战：高保真仿真场景重建成本高昂、仿真中系统性策略评估复杂度高，以及真实世界部署兼容性差。为应对这些挑战，我们提出了 BestMan——一个可扩展且支持无缝实-仿-实迁移的平台，弥合仿真与现实之间的鸿沟，从而有效支持家庭移动操作任务的策略开发、集成与部署。具体而言，我们设计了一种新型自动场景生成（Automated Scene Generation, ASG）模块，从真实观测中重建逼真的仿真环境；提出一种仿真引导的任务形式化与技能学习架构，支持在仿真中灵活集成并大规模评估混合技能策略；最后，为提升真实世界可扩展性，我们开发了硬件无关且统一的中间件（Hardware-agnostic and Unified Middleware, HUM），确保跨异构移动操作平台的仿真到现实迁移过程无缝且兼容。实验结果表明，本平台在构建标准化基准及推动移动操作领域前沿研究方面展现出优越性能。

Mobile manipulation is a fundamental capability in embodied intelligence robotics. The growing demand for robust and generalizable manipulation in unstructured household environments has driven rapid progress in embodied intelligence platforms. However, achieving a seamless transfer across the real-to-sim-to-real cycle faces three key challenges, including costly high-fidelity simulation scenes reconstruction, the complexity of systematic strategy evaluation in simulation, and incompatible real-world deployments. To address these challenges, we develop BestMan, a scalable and seamless real-to-sim-to-real platform that bridges the gap between the simulation and the real world, enabling effective strategy development, integration, and deployment for household mobile manipulation. Specifically, we design a novel Automated Scene Generation (ASG) module to reconstruct realistic simulations from real observations. Then, we propose a simulation-guided task formalization and skill learning architecture that supports the flexible integration and large-scale evaluations of hybrid skill strategies in simulation. Finally, to enhance the real-world scalability, we develop a Hardware-agnostic and Unified Middleware (HUM) to ensure seamless and compatible sim-to-real transfer across heterogeneous mobile manipulators for real deployments. Experimental results demonstrate the superior performance of our proposed platform in establishing standardized benchmarks and facilitating promising research in the field of mobile manipulation.