  In recent years, reinforcement learning and imitation learning have shown
great potential for controlling humanoid robots' motion. However, these methods
typically create simulation environments and rewards for specific tasks,
resulting in the requirements of multiple policies and limited capabilities for
tackling complex and unknown tasks. To overcome these issues, we present a
novel approach that combines adversarial imitation learning with large language
models (LLMs). This innovative method enables the agent to learn reusable
skills with a single policy and solve zero-shot tasks under the guidance of
LLMs. In particular, we utilize the LLM as a strategic planner for applying
previously learned skills to novel tasks through the comprehension of
task-specific prompts. This empowers the robot to perform the specified actions
in a sequence. To improve our model, we incorporate codebook-based vector
quantization, allowing the agent to generate suitable actions in response to
unseen textual commands from LLMs. Furthermore, we design general reward
functions that consider the distinct motion features of humanoid robots,
ensuring the agent imitates the motion data while maintaining goal orientation
without additional guiding direction approaches or policies. To the best of our
knowledge, this is the first framework that controls humanoid robots using a
single learning policy network and LLM as a planner. Extensive experiments
demonstrate that our method exhibits efficient and adaptive ability in
complicated motion tasks.
