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arXiv · 2606.01063

MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention

Ruoxuan Zhang·Qiaoqiao Wan·Zhengguang Wang·Chenghao Yu·Hongxia Xie·Wen-Huang Cheng·Jianlong Fu
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Abstract

Theory-of-Mind (ToM) reasoning enables embodied agents to understand human beliefs, goals, and intentions, but existing benchmarks mainly evaluate this ability through offline question answering or scenario-level action prediction. MindPower advances embodied ToM by introducing robot-centric reasoning from perception to action; however, it does not evaluate whether an agent can continuously interact with a changing environment and intervene only when assistance is needed. Building on MindPower, we introduce the MindHelper Challenge, which extends embodied ToM evaluation to real-time closed-loop precision intervention. An agent must continuously observe the environment, maintain actor-specific beliefs, identify when a human requires assistance, generate executable actions, and remain silent when intervention is unnecessary. We further propose MindClaw, a simple yet effective Claw-style framework that integrates an actor-specific Belief Table, embodied cognitive skills, and a Trigger-based cognitive dispatcher. Experiments show that MindClaw achieves 36.63% precise intervention rate and 14.36\% task accuracy, substantially outperforming direct VLM baselines, whose corresponding results remain below 12.05% and 3.80%.

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Ruoxuan Zhang, Qiaoqiao Wan, Zhengguang Wang, Chenghao Yu, Hongxia Xie, Wen-Huang Cheng, Jianlong Fu. 2026-05-31. MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention. https://arxiv.org/abs/2606.01063

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