使用大语言模型的轨迹自适应
Trajectory Adaptation using Large Language Models
AI总结：
针对机器人轨迹需按人类指令适配新情况的问题，提出基于预训练大语言模型生成代码策略调整轨迹路点的框架，无需任务特定训练，在多仿真环境验证其适配复杂指令的有效性。
摘要：
根据新情况并基于人类指令调整机器人轨迹，对于实现更直观、可扩展的人机交互至关重要。本研究提出一种灵活的基于语言的框架，用于调整由现成运动规划器（如RRT、A-star等）生成或从人类演示中学习的通用机器人轨迹。我们利用预训练大语言模型（LLMs）通过生成代码作为密集机器人操作的策略来调整轨迹路点，从而支持比现有方法更复杂、灵活的指令。该方法允许我们纳入更广泛的命令类型，包括数值输入。与需要训练的最先进特征基序列到序列模型相比，我们的方法无需任务特定训练，并提供更强的可解释性和更有效的反馈机制。我们通过在Pybullet和Gazebo仿真环境中对机器人 manipulator、空中飞行器和地面机器人进行仿真实验验证了该方法，证明LLMs可成功将轨迹适配于复杂人类指令。
英文摘要：
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.
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。
