  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/.
