MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation
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 ...
| Authors | Markus Knauer, Edoardo Fiorini, Maximilian Mühlbauer et al. |
|---|---|
| Primary category | cs.RO |
| Published | 22 April 2026 |
| Last updated at source | 27 August 2026 |
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