DualTHOR: A Dual-Arm Humanoid Simulation Platform for Contingency-Aware Planning

TL;DR

DualTHOR is a physics-based simulation platform for dual-arm humanoid robots that incorporates real-world assets and failure contingencies, aiming to improve the robustness and transferability of embodied AI models in household tasks.

Contribution

We introduce DualTHOR, a comprehensive simulation platform with contingency mechanisms for dual-arm humanoid robots, addressing limitations of previous simulators and enhancing embodied AI research.

Findings

Current VLMs struggle with dual-arm coordination.

VLMs show limited robustness in realistic environments.

DualTHOR enables better evaluation of VLM robustness.

Abstract

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.