MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation

TL;DR

This paper introduces MOMO, an integrated framework enabling industrial robots to adapt skills seamlessly through kinesthetic, verbal, and graphical interactions, enhancing flexibility and user-friendliness.

Contribution

It presents a novel multi-modal interaction framework combining five components for safe, flexible, and intuitive robot skill adaptation in industrial environments.

Findings

Validated on a 7-DoF robot at Automatica 2025

Demonstrated natural language-based surface finishing

Showed generalization of skill adaptation across modalities

Abstract

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.