Industrial robot applications require increasingly flexible systems that non-expert users can easily adapt for varying tasks and environments. However, different adaptations benefit from different interaction modalities. We present an interactive framework that enables robot skill adaptation through three complementary modalities: kinesthetic touch for precise spatial corrections, natural language for high-level semantic modifications, and a graphical web interface for visualizing geometric relations and trajectories, inspecting and adjusting parameters, and editing via-points by drag-and-drop. The framework integrates five components: energy-based human-intention detection, a tool-based LLM architecture (where the LLM selects and parameterizes predefined functions rather than generating code) for safe natural language adaptation, Kernelized Movement Primitives (KMPs) for motion encoding, probabilistic Virtual Fixtures for guided demonstration recording, and ergodic control for surface finishing. We demonstrate that this tool-based LLM architecture generalizes skill adaptation from KMPs to ergodic control, enabling voice-commanded surface finishing. Validation on a 7-DoF torque-controlled robot at the Automatica 2025 trade fair demonstrates the practical applicability of our approach in industrial settings.

翻译：工业机器人应用需要日益灵活的智能系统，以便非专业用户能够轻松适应不同任务及环境。然而，不同的适配需求受益于不同的交互模式。我们提出了一种交互式框架，通过三种互补的模态实现机器人技能适配：力觉引导触觉模态用于精确的空间校正，自然语言模态用于高层语义修改，以及图形化网络界面模态用于可视化几何关系与轨迹、检查调整参数、并通过拖拽编辑途经点。该框架整合了五个组件：基于能量的人体意图检测、基于工具的大语言模型架构（其中大语言模型选择并参数化预定义函数而非生成代码）以实现安全的自然语言适配、核化运动基元用于运动编码、概率虚拟夹具用于引导式示教记录、以及遍历控制用于表面精加工。我们证明了该基于工具的LLM架构能够将技能适配从核化运动基元推广至遍历控制，从而支持语音指令驱动的表面精加工。在2025年Automatica贸易展上，通过7自由度力矩控制机器人的验证实验，证明了该方法在工业场景中的实际应用性。