  We present a large language model (LLM) based system to empower quadrupedal
robots with problem-solving abilities for long-horizon tasks beyond short-term
motions. Long-horizon tasks for quadrupeds are challenging since they require
both a high-level understanding of the semantics of the problem for task
planning and a broad range of locomotion and manipulation skills to interact
with the environment. Our system builds a high-level reasoning layer with large
language models, which generates hybrid discrete-continuous plans as robot code
from task descriptions. It comprises multiple LLM agents: a semantic planner
for sketching a plan, a parameter calculator for predicting arguments in the
plan, and a code generator to convert the plan into executable robot code. At
the low level, we adopt reinforcement learning to train a set of motion
planning and control skills to unleash the flexibility of quadrupeds for rich
environment interactions. Our system is tested on long-horizon tasks that are
infeasible to complete with one single skill. Simulation and real-world
experiments show that it successfully figures out multi-step strategies and
demonstrates non-trivial behaviors, including building tools or notifying a
human for help.
