DA-GRD: 知覚と実行の不一致下での触覚による把持回復のための意思決定を考慮した把持関連曖昧性解消
DA-GRD: Decision-Aware Grasp-Relevant Disambiguation for tactile recovery under perception-to-execution mismatches
物体が動いて視覚ベースの把持位置がずれた際に、少数の触覚プローブで候補を絞り込み、実行可能な把持を回復する手法を提案。
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: Haoran Wang, Yuteng Sun, Yuanjie Li, Ruofei Bai, Meng Yee, Chuah, Wenyu Liang, Jun Li, Wei-Yun Yau
分類: cs.RO
原文アブストラクト
Grasping is a fundamental robotic capability that bridges perception and physical task execution. This paper studies grasp pose recovery under a perception-to-execution mismatch, where a grasp generated from visual perception may become spatially stale if the object moves before execution, using only sparse tactile interactions and no further visual observations. We propose DA-GRD, Decision-Aware Grasp-Relevant Disambiguation, which maintains a weighted planar belief over possible object configurations and selects tactile probes according to their ability to eliminate hypotheses and improve agreement among candidate task grasps. Rather than fully relocalizing the object, DA-GRD stops when the remaining hypotheses support a common executable grasp. In MuJoCo experiments on ten rigid objects with translations up to 5~cm and yaw perturbations up to $\pm45^\circ$, DA-GRD achieves an 84.7% physical lift success rate, compared with 9.1% for stale AnyGrasp, 21.2% for the original fix-scan baseline, and 63.7% for fix-scan method adapted with an SE(2) belief. DA-GRD also achieves a 57.3% Task conditioned Success rate. Across objects, it uses a success-average of 4.13 tactile probes over the ten per-object means, corresponding to a 72.5% reduction relative to the fixed 15-probe baselines. Real-world experiments on six objects achieve 71.7% physical lift success and 38.3% task-conditioned success with 4.20 probes on average. These results show that tactile sensing can recover task-relevant grasps under vision-off conditions with limited physical interaction, without requiring complete object localization.
関連論文
- ME-Dex 1.0:異種触覚センシングを世界行動モデリングへ統合触覚/マニピュレーション
- Touch2Trace: 触覚駆動型模倣学習による巧みなケーブルトレース触覚/マニピュレーション
- TactileReflex: ノイズ統計駆動の視覚-触覚反射制御による力感応マニピュレーション触覚/マニピュレーション