用大模型自动切换机械臂操作模式，减少手动操作负担。
LAMS: LLM-Driven Automatic Mode Switching for Assistive Teleoperation
- 基于大模型理解任务上下文，自动决定模式切换
- 无需事前示范，通过用户反馈逐步提升准确率
- 实测显著减少手动切换次数，适合复杂长程任务
通过低自由度控制器（如操纵杆）远程操控高自由度机器人时，常需频繁切换控制模式，每种模式将操纵杆动作映射到特定机器人行为。手动频繁切换使操作繁琐低效。现有自动模式切换方法（如基于启发式或学习的方法）通常仅适用于特定任务，泛化能力差。本文提出基于大语言模型的自动模式切换方法LAMS，能根据任务上下文自动切换控制模式，无需前期任务示范，并通过用户生成的切换样本持续优化。在包含10名参与者的消融实验和用户研究中，验证了LAMS可有效减少手动切换、优于替代方案，且性能随使用时间提升。项目网站与补充材料见https://lams-assistance.github.io/。
原文摘要 · Abstract (English)
Teleoperating high degrees-of-freedom (DoF) robotic manipulators via low-DoF controllers like joysticks often requires frequent switching between control modes, where each mode maps controller movements to specific robot actions. Manually performing this frequent switching can make teleoperation cumbersome and inefficient. On the other hand, existing automatic mode-switching solutions, such as heuristic-based or learning-based methods, are often task-specific and lack generalizability. In this paper, we introduce LLM-Driven Automatic Mode Switching (LAMS), a novel approach that leverages Large Language Models (LLMs) to automatically switch control modes based on task context. Unlike existing methods, LAMS requires no prior task demonstrations and incrementally improves by integrating user-generated mode-switching examples. We validate LAMS through an ablation study and a user study with 10 participants on complex, long-horizon tasks, demonstrating that LAMS effectively reduces manual mode switches, is preferred over alternative methods, and improves performance over time. The project website with supplementary materials is at https://lams-assistance.github.io/.
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