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arXiv · 2510.07882

Towards Proprioception-Aware Embodied Planning for Dual-Arm Humanoid Robots

Boyu Li·Siyuan He·Hang Xu·Haoqi Yuan·Xinrun Xu·Yu Zang·Liwei Hu·Junpeng Yue·Zhenxiong Jiang·Pengbo Hu·Börje F. Karlsson·Yehui Tang·Zongqing Lu
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Abstract

In recent years, Multimodal Large Language Models (MLLMs) have demonstrated the ability to serve as high-level planners, enabling robots to follow complex human instructions. However, their effectiveness, especially in long-horizon tasks involving dual-arm humanoid robots, remains limited. This limitation arises from two main challenges: (i) the absence of simulation platforms that systematically support task evaluation and data collection for humanoid robots, and (ii) the insufficient embodiment awareness of current MLLMs, which hinders reasoning about dual-arm selection logic and body positions during planning. To address these issues, we present DualTHOR, a new dual-arm humanoid simulator, with continuous transition and a contingency mechanism. Building on this platform, we propose Proprio-MLLM, a model that enhances embodiment awareness by incorporating proprioceptive information with motion-based position embedding and a cross-spatial encoder. Experiments show that, while existing MLLMs struggle in this environment, Proprio-MLLM achieves an average improvement of 19.75% in planning performance. Our work provides both an essential simulation platform and an effective model to advance embodied intelligence in humanoid robotics. The code is available at https://anonymous.4open.science/r/DualTHOR-5F3B.

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Boyu Li, Siyuan He, Hang Xu, Haoqi Yuan, Xinrun Xu, Yu Zang, Liwei Hu, Junpeng Yue, Zhenxiong Jiang, Pengbo Hu, Börje F. Karlsson, Yehui Tang, Zongqing Lu. 2025-10-09. Towards Proprioception-Aware Embodied Planning for Dual-Arm Humanoid Robots. https://arxiv.org/abs/2510.07882

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