  When humans create sculptures, we are able to reason about how geometrically
we need to alter the clay state to reach our target goal. We are not computing
point-wise similarity metrics, or reasoning about low-level positioning of our
tools, but instead determining the higher-level changes that need to be made.
In this work, we propose LLM-Craft, a novel pipeline that leverages large
language models (LLMs) to iteratively reason about and generate
deformation-based crafting action sequences. We simplify and couple the state
and action representations to further encourage shape-based reasoning. To the
best of our knowledge, LLM-Craft is the first system successfully leveraging
LLMs for complex deformable object interactions. Through our experiments, we
demonstrate that with the LLM-Craft framework, LLMs are able to successfully
reason about the deformation behavior of elasto-plastic objects. Furthermore,
we find that LLM-Craft is able to successfully create a set of simple letter
shapes. Finally, we explore extending the framework to reaching more ambiguous
semantic goals, such as "thinner" or "bumpy". For videos please see our
website: https://sites.google.com/andrew.cmu.edu/llmcraft.
