Developing embodied agents capable of performing complex interactive tasks in real-world scenarios remains a fundamental challenge in embodied AI. Although recent advances in simulation platforms have greatly enhanced task diversity to train embodied Vision Language Models (VLMs), most platforms rely on simplified robot morphologies and bypass the stochastic nature of low-level execution, which limits their transferability to real-world robots. To address these issues, we present a physics-based simulation platform DualTHOR for complex dual-arm humanoid robots, built upon an extended version of AI2-THOR. Our simulator includes real-world robot assets, a task suite for dual-arm collaboration, and inverse kinematics solvers for humanoid robots. We also introduce a contingency mechanism that incorporates potential failures through physics-based low-level execution, bridging the gap to real-world scenarios. Our simulator enables a more comprehensive evaluation of the robustness and generalization of VLMs in household environments. Extensive evaluations reveal that current VLMs struggle with dual-arm coordination and exhibit limited robustness in realistic environments with contingencies, highlighting the importance of using our simulator to develop more capable VLMs for embodied tasks. The code is available at https://github.com/ds199895/DualTHOR.git.

翻译：开发能够在真实世界场景中执行复杂交互任务的具身智能体，仍然是具身人工智能领域的核心挑战。尽管仿真平台的最新进展极大地丰富了用于训练具身视觉语言模型的任务多样性，但大多数平台依赖于简化的机器人形态，并绕过了底层执行过程中的随机性，这限制了其向真实世界机器人的可迁移性。为解决这些问题，我们提出了一个基于物理仿真的平台DualTHOR，专为复杂的双臂人形机器人设计，该平台构建于AI2-THOR的扩展版本之上。我们的仿真器包含真实世界机器人资产、一套面向双臂协作的任务集，以及用于人形机器人的逆运动学求解器。我们还引入了一种应急机制，通过基于物理的底层执行来纳入潜在的失败情况，从而弥合了与真实世界场景的差距。我们的仿真器能够更全面地评估视觉语言模型在家庭环境中的鲁棒性和泛化能力。大量评估表明，当前的视觉语言模型在双臂协调方面存在困难，并且在具有应急情况的真实环境中表现出有限的鲁棒性，这凸显了使用我们的仿真器来开发更具能力的具身任务视觉语言模型的重要性。代码发布于https://github.com/ds199895/DualTHOR.git。