内语作为行为导向：用于人机协调的可调imitation

有效的人机协调需要能够展现和响应类人行为的的人工智能体，同时能够适应不断变化的情境。imitation学习（imitation learning）作为构建此类智能体的一种方法，通过训练它们模仿人类演示的行为而备受推崇。然而，当前方法难以捕捉人类行为固有的多样性和非马尔可夫性，缺乏在推理阶段对行为进行调控的能力。drawing inspiration from the theory of human cognitive processes, where inner speech guides action selection before execution, we propose MIMIC (Modeling Inner Motivations for Imitation and Control), a framework that uses language as an internal representation of behavioral intent. MIMIC employs the novel use of vision-language models as linguistic scaffolding to train a conditional variational autoencoder capable of generating inner speech from observations. A diffusion-based behavior cloning policy then selects actions conditioned on current observations and the generated inner speech. MIMIC enables fine-grained steering of behavior at inference time by conditioning the agent on behavior-specific speech. Experiments across robotic manipulation tasks and human-AI collaboration games demonstrate that MIMIC significantly enhances both behavior diversity and fidelity to human demonstrations while enabling nuanced behavioral steering without training on additional demonstrations. We open source our code and provide pre-trained MIMIC agents and qualitative demos at: https://mimic-research.github.io.