  Learning reward functions for physical skills are challenging due to the vast
spectrum of skills, the high-dimensionality of state and action space, and
nuanced sensory feedback. The complexity of these tasks makes acquiring expert
demonstration data both costly and time-consuming. Large Language Models (LLMs)
contain valuable task-related knowledge that can aid in learning these reward
functions. However, the direct application of LLMs for proposing reward
functions has its limitations such as numerical instability and inability to
incorporate the environment feedback. We aim to extract task knowledge from
LLMs using environment feedback to create efficient reward functions for
physical skills. Our approach consists of two components. We first use the LLM
to propose features and parameterization of the reward function. Next, we
update the parameters of this proposed reward function through an iterative
self-alignment process. In particular, this process minimizes the ranking
inconsistency between the LLM and our learned reward functions based on the new
observations. We validated our method by testing it on three simulated physical
skill learning tasks, demonstrating effective support for our design choices.
