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：摘要：工业机器人应用需要日益灵活的系统，使非专业用户能够轻松针对不同任务和环境进行调整。然而，不同的适应性调整需要借助不同的交互模态。我们提出了一种交互式框架，通过三种互补模态实现机器人技能适应：用于精确空间校正的动觉示教、用于高层语义修改的自然语言，以及用于可视化几何关系与轨迹、检查调整参数以及通过拖拽编辑途经点的图形化网页界面。该框架集成了五个组件：基于能量的人体意图检测、基于工具的LLM架构（其中LLM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应、用于运动编码的核化运动基元（KMPs）、用于引导式演示记录的随机虚拟夹具，以及用于表面精整的遍历控制。我们证明了这种基于工具的LLM架构能将技能适应从KMPs推广至遍历控制，实现语音指令驱动的表面精整。在2025年自动化博览会（Automatica 2025）上，基于七自由度力矩控制机器人的验证，展示了该方法在工业场景中的实际应用价值。