{
  "id": 5503,
  "url": "https://arxiv.org/abs/2604.20468v2",
  "title": "MOMO: A framework for seamless physical, verbal, and graphical robot skill learning and adaptation",
  "summary": "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 rela",
  "authors": "Markus Knauer, Edoardo Fiorini, Maximilian Mühlbauer, Stefan Schneyer, Promwat Angsuratanawech, Florian Samuel Lay et al.",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-04-22T11:54:54.000Z",
  "fetched_at": "2026-07-14T16:31:48.874Z",
  "source_slug": "arxiv-ethics",
  "source_name": "arXiv",
  "source_homepage": "https://arxiv.org",
  "ethics_ai_record_url": "https://ethics.ai/record/5503",
  "original_url": "https://arxiv.org/abs/2604.20468v2",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}