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 atthis https URL.
View on arXiv@article{li2025_2506.16012,
title={ DualTHOR: A Dual-Arm Humanoid Simulation Platform for Contingency-Aware Planning },
author={ Boyu Li and Siyuan He and Hang Xu and Haoqi Yuan and Yu Zang and Liwei Hu and Junpeng Yue and Zhenxiong Jiang and Pengbo Hu and Börje F. Karlsson and Yehui Tang and Zongqing Lu },
journal={arXiv preprint arXiv:2506.16012},
year={ 2025 }
}