GUIDER: 実ロボットデータを用いた遠隔操作における目標非依存の人間意図推論の評価
GUIDER: Evaluating Goal-Free Human Intent Inference for Teleoperated Manipulation on Real-Robot Data
本論文は、ロボットアームの遠隔操作中に人間の意図を確率的に推論するフレームワークGUIDERを実データで評価し、全シナリオで正しい把持候補を推定できたことを示した。
詳しい要約
1. どんなもの?
2. 先行研究と比べてどこがすごい?
3. 技術・手法の肝は?
4. どうやって有効だと検証した?
5. 議論はある?
6. 次に読むべき論文は?
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Nicholas Kenny, Cesar Alan Contreras, Basile Ouedraogo, Rustam Stolkin, Manolis Chiou, Maria Kyrarini
分類: cs.RO, cs.HC
原文アブストラクト
This paper presents an evaluation of a goal-free probabilistic framework for human intent inference during robotic manipulation. We deploy the Global User Intent Dual-phase Estimation for Robots (GUIDER) on data collected from a robotic arm to test the manipulation phase across various assistance scenarios, including making tea and fetching medicine. To support operation, we add online probability updates, workspace limits, support-plane filtering, and a grasping mode that prioritizes feasible grasp regions, all of which are tested on the recorded data while preserving its original temporal conditions. Across 20 manipulation steps in three scenarios, GUIDER estimated human intent within the correct grasp-candidate set in all cases and achieved a time to confident prediction of 3.7 s, a remaining time before first grasp of 49.6 s, a prediction stability of 96.4%, and a runtime of 4.857/4.474 s (mean/median) per perceptual phase of intent.