  Large Language Models (LLMs) have been successful at generating robot policy
code, but so far these results have been limited to high-level tasks that do
not require precise movement. It is an open question how well such approaches
work for tasks that require reasoning over contact forces and working within
tight success tolerances. We find that, with the right action space, LLMs are
capable of successfully generating policies for a variety of contact-rich and
high-precision manipulation tasks, even under noisy conditions, such as
perceptual errors or grasping inaccuracies. Specifically, we reparameterize the
action space to include compliance with constraints on the interaction forces
and stiffnesses involved in reaching a target pose. We validate this approach
on subtasks derived from the Functional Manipulation Benchmark (FMB) and NIST
Task Board Benchmarks. Exposing this action space alongside methods for
estimating object poses improves policy generation with an LLM by greater than
3x and 4x when compared to non-compliant action spaces
