MOMO：一种无缝物理、语言和图形机器人技能学习与适应框架
MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation
AI总结：
本文提出MOMO框架，通过三种互补交互方式实现机器人技能的灵活适应，结合触觉、自然语言和图形界面，提升非专家用户在工业环境中的应用能力。
摘要：
工业机器人应用需要更加灵活的系统，使非专家用户能够轻松适应不同任务和环境。然而，不同的适应需求受益于不同的交互模式。本文提出一个交互框架，通过三种互补的交互模式实现机器人技能的适应：触觉用于精确的空间校正，自然语言用于高层语义修改，图形网页界面用于可视化几何关系和轨迹，检查和调整参数，以及通过拖放编辑路径点。该框架整合了五个组件：基于能量的人意图检测，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）用于安全的自然语言适应，内核化运动基元（KMPs）用于运动编码，概率虚拟固定装置用于引导演示记录，以及极限控制用于表面处理。我们证明了这种基于工具的LLM架构能够将技能适应从KMPs推广到极限控制，从而实现语音命令的表面处理。在Automatica 2025贸易展上对7自由度扭矩控制机器人的验证展示了我们的方法在工业环境中的实际适用性。
英文摘要：
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.
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