{
  "annotation_id": "5e0b1d99-30d9-4975-b5a8-a8ff9663d7d9",
  "date_created": "2026-02-17T05:53:07.760000Z",
  "date_modified": "2026-02-17T05:53:07.760000Z",
  "file_hash": "af12a86e2bdb386cc3b520e6cd61c061374eb1f625f9a9b2dd2f2a3a8b3b1a43",
  "private": false,
  "record": {
    "abstract": "Whenever humans and robots work together, it is essential that unexpected robot behavior can be explained to the user. Especially in applications such as shared control the user and the robot must share the same model of the objects in the world, and the actions that can be performed on these objects.\n  In this paper, we achieve this with a so-called model reconciliation framework. We leverage a Large Language Model to predict and explain the difference between the robot\u0027s and the human\u0027s mental models, without the need of a formal mental model of the user. Furthermore, our framework aims to solve the model divergence after the explanation by allowing the human to correct the robot. We provide an implementation in an assistive robotics domain, where we conduct a set of experiments with a real wheelchair-based mobile manipulator and its digital twin.",
    "arxiv_id": "2601.06552",
    "authors": [
      "Britt Besch",
      "Tai Mai",
      "Jeremias Thun",
      "Markus Huff",
      "J\u00f6rn Vogel",
      "Freek Stulp",
      "Samuel Bustamante"
    ],
    "categories": [
      "cs.RO",
      "cs.HC"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Model Reconciliation through Explainability and Collaborative Recovery in Assistive Robotics",
    "url": "https://arxiv.org/abs/2601.06552",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "be1f80a5-07f0-4a0a-8775-9d23c5f300c3",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}