DualTHOR: A Dual-Arm Humanoid Simulation Platform for Contingency-Aware
  Planning
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.
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Boyu Li
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Siyuan He
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Hang Xu
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Haoqi Yuan
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2025-07-22T08:33:32.710Z
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Haoqi Yuan
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Yaya041
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Yu Zang
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Liwei Hu
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Junpeng Yue
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Zhenxiong Jiang
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Pengbo Hu
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Börje F. Karlsson
claimed_verified
2025-06-26T09:23:24.327Z
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Börje Karlsson
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tellarin
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Yehui Tang
claimed_verified
2025-06-26T09:23:21.125Z
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YehuiTang
user
WizardTY
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Zongqing Lu
DualTHOR: A Dual-Arm Humanoid Simulation Platform for Contingency-Aware
  Planning
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.
A simulator named DualTHOR for training dual-arm humanoid robots integrates real-world assets and physics to enhance the robustness and generalization of Vision Language Models.
embodied AI Vision Language Models VLMs physics-based simulation DualTHOR AI2-THOR dual-arm robots real-world robot assets task suite inverse kinematics solvers contingency mechanism physics-based low-level execution