Large language models (LLMs) have shown significant potential for robotics applications, particularly task planning, by harnessing their language comprehension and text generation capabilities. However, in applications such as household robotics, a critical gap remains in the personalization of these models to individual user preferences. We introduce LLM-Personalize, a novel framework with an optimization pipeline designed to personalize LLM planners for household robotics. Our LLM-Personalize framework features an LLM planner that performs iterative planning in multi-room, partially-observable household scenarios, making use of a scene graph constructed with local observations. The generated plan consists of a sequence of high-level actions which are subsequently executed by a controller. Central to our approach is the optimization pipeline, which combines imitation learning and iterative self-training to personalize the LLM planner. In particular, the imitation learning phase performs initial LLM alignment from demonstrations, and bootstraps the model to facilitate effective iterative self-training, which further explores and aligns the model to user preferences. We evaluate LLM-Personalize on Housekeep, a challenging simulated real-world 3D benchmark for household rearrangements, and show that LLM-Personalize achieves more than a 30 percent increase in success rate over existing LLM planners, showcasing significantly improved alignment with human preferences. Project page: https://donggehan.github.io/projectllmpersonalize/.

翻译：大语言模型（LLMs）凭借其语言理解与文本生成能力，在机器人应用（尤其是任务规划）中展现出巨大潜力。然而在家庭机器人等场景中，现有模型在个性化适配个体用户偏好方面仍存在关键缺陷。我们提出LLM-Personalize框架，该框架包含一个优化流程，旨在实现家庭机器人场景下LLM规划器的个性化定制。LLM-Personalize框架的核心是一个面向多房间部分可观测家庭场景的迭代规划器，它利用局部观测构建的场景图谱进行规划。生成的规划由一系列高层动作组成，最终由控制器执行。本方法的关键在于融合模仿学习与迭代自训练的优化流程：模仿学习阶段通过示范实现LLM的初步对齐，并引导模型进入有效的迭代自训练阶段，该阶段将进一步探索并适配用户偏好。我们在家庭重排任务的挑战性真实3D仿真基准Housekeep上评估了LLM-Personalize，结果显示其成功率较现有LLM规划器提升超过30%，显著增强了与人类偏好的对齐程度。项目主页：https://donggehan.github.io/projectllmpersonalize/。