Long-horizon Locomotion and Manipulation on a Quadrupedal Robot with Large Language Models
<|reference_start|>Long-horizon Locomotion and Manipulation on a Quadrupedal Robot with Large Language Models: 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. Demos are available on our project page: https://sites.google.com/view/long-horizon-robot.<|reference_end|>
@article{ouyang2024long-horizon,
    title={Long-horizon Locomotion and Manipulation on a Quadrupedal Robot with
  Large Language Models},
    author={Yutao Ouyang, Jinhan Li, Yunfei Li, Zhongyu Li, Chao Yu, Koushil
  Sreenath, Yi Wu},
    journal={arXiv preprint arXiv:2404.05291},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.05291},
    primaryClass={cs.RO}
}