用大模型直接改机器人路径，让指令更灵活直观。
Trajectory Adaptation using Large Language Models
- 用预训练大模型生成代码来调整路径点，无需额外训练。
- 在仿真环境中成功应对复杂人类指令，适应多种机器人类型。
- 支持数值指令和可解释反馈，适合交互式机器人开发。
根据新情境下的人类指令调整机器人轨迹，是实现更自然、可扩展人机交互的关键。本文提出一种基于语言的灵活框架，用于调整由现成运动规划器（如RRT、A-star）或人类示范学习得到的通用机器人轨迹。我们利用预训练大模型生成代码作为策略，对密集机器人操作的路径点进行调整，支持比现有方法更复杂灵活的指令，包括数值输入。与需训练的特征序列到序列模型相比，该方法无需任务特定训练，具有更高可解释性及更有效的反馈机制。我们在PyBullet和Gazebo仿真环境中对机械臂、无人机和地面机器人进行了验证，结果表明大模型能成功将轨迹适配至复杂人类指令。
原文摘要 · Abstract (English)
Adapting robot trajectories based on human instructions as per new situations is essential for achieving more intuitive and scalable human-robot interactions. This work proposes a flexible language-based framework to adapt generic robotic trajectories produced by off-the-shelf motion planners like RRT, A-star, etc, or learned from human demonstrations. We utilize pre-trained LLMs to adapt trajectory waypoints by generating code as a policy for dense robot manipulation, enabling more complex and flexible instructions than current methods. This approach allows us to incorporate a broader range of commands, including numerical inputs. Compared to state-of-the-art feature-based sequence-to-sequence models which require training, our method does not require task-specific training and offers greater interpretability and more effective feedback mechanisms. We validate our approach through simulation experiments on the robotic manipulator, aerial vehicle, and ground robot in the Pybullet and Gazebo simulation environments, demonstrating that LLMs can successfully adapt trajectories to complex human instructions.
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