MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation
This addresses the need for more adaptable industrial robot systems for non-expert users, though it appears incremental as it combines existing methods like LLMs and movement primitives.
The researchers tackled the problem of enabling non-expert users to adapt robot skills flexibly across varying industrial tasks and environments, resulting in a framework that integrates kinesthetic touch, natural language, and graphical interfaces for practical application on a 7-DoF robot at the Automatica 2025 trade fair.
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.