基于大语言模型的四足机器人长时程运动与操作
Long-horizon Locomotion and Manipulation on a Quadrupedal Robot with Large Language Models
AI总结：
本研究提出基于大语言模型的四足机器人系统，通过多LLM智能体实现高层任务规划与代码生成，结合强化学习训练的底层运动技能，可完成需多步策略的长时程运动与操作任务，在仿真和真实场景中均验证有效。
摘要：
我们提出了一种基于大语言模型（LLM）的系统，赋予四足机器人解决长时程任务的能力，使其能力超越短期运动范畴。四足机器人的长时程任务极具挑战性，因为这类任务既需要对任务规划的问题语义有高层理解，又需要具备多样的运动与操作技能以与环境交互。我们的系统利用大语言模型构建了高层推理层，可根据任务描述生成混合离散-连续规划，转化为机器人代码。该系统包含多个LLM智能体：负责勾勒规划方案的语义规划器、预测规划中参数的参数计算器、将规划转换为可执行机器人代码的代码生成器，以及处理执行失败或人工干预的重规划器。在底层，我们采用强化学习训练了一套运动规划与控制技能，充分发挥四足机器人的灵活性，实现丰富的环境交互。我们在无法通过单一技能完成的长时程任务上测试了该系统。仿真与真实世界实验表明，该系统成功制定出多步策略，并展现出非平凡的行为，包括制作工具或向人类求助。演示视频可在我们的项目页面查看：https://sites.google.com/view/long-horizon-robot。
英文摘要：
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 that sketches a plan, a parameter calculator that predicts arguments in the plan, a code generator that converts the plan into executable robot code, and a replanner that handles execution failures or human interventions. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
