{"ID":2894792,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.10131","arxiv_id":"2507.10131","title":"Probabilistic Human Intent Prediction for Mobile Manipulation: An Evaluation with Human-Inspired Constraints","abstract":"Accurate inference of human intent enables human-robot collaboration without constraining human control or causing conflicts between humans and robots. We present GUIDER (Global User Intent Dual-phase Estimation for Robots), a probabilistic framework that enables a robot to estimate the intent of human operators. GUIDER maintains two coupled belief layers, one tracking navigation goals and the other manipulation goals. In the Navigation phase, a Synergy Map blends controller velocity with an occupancy grid to rank interaction areas. Upon arrival at a goal, an autonomous multi-view scan builds a local 3D cloud. The Manipulation phase combines U2Net saliency, FastSAM instance saliency, and three geometric grasp-feasibility tests, with an end-effector kinematics-aware update rule that evolves object probabilities in real-time. GUIDER can recognize areas and objects of intent without predefined goals. We evaluated GUIDER on 25 trials (five participants x five task variants) in Isaac Sim, and compared it with two baselines, one for navigation and one for manipulation. Across the 25 trials, GUIDER achieved a median stability of 93-100% during navigation, compared with 60-100% for the BOIR baseline, with an improvement of 39.5% in a redirection scenario (T5). During manipulation, stability reached 94-100% (versus 69-100% for Trajectron), with a 31.4% difference in a redirection task (T3). In geometry-constrained trials (manipulation), GUIDER recognized the object intent three times earlier than Trajectron (median remaining time to confident prediction 23.6 s vs 7.8 s). These results validate our dual-phase framework and show improvements in intent inference in both phases of mobile manipulation tasks.","short_abstract":"Accurate inference of human intent enables human-robot collaboration without constraining human control or causing conflicts between humans and robots. We present GUIDER (Global User Intent Dual-phase Estimation for Robots), a probabilistic framework that enables a robot to estimate the intent of human operators. GUIDE...","url_abs":"https://arxiv.org/abs/2507.10131","url_pdf":"https://arxiv.org/pdf/2507.10131v1","authors":"[\"Cesar Alan Contreras\",\"Manolis Chiou\",\"Alireza Rastegarpanah\",\"Michal Szulik\",\"Rustam Stolkin\"]","published":"2025-07-14T10:21:27Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.CV\",\"cs.HC\"]","methods":"[]","has_code":false}