SemanticFeels: ハンド内操作中の意味的ラベリング
SemanticFeels: Semantic Labeling during In-Hand Manipulation
視覚と触覚から物体の形状と素材を同時に認識するため、ニューラル暗黙形状表現に意味的ラベリングを統合したフレームワークを提案し、多素材物体で平均79.87%の素材分類精度を達成した。
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: Anas Al Shikh Khalil, Haozhi Qi, Roberto Calandra
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
As robots become increasingly integrated into everyday tasks, their ability to perceive both the shape and properties of objects during in-hand manipulation becomes critical for adaptive and intelligent behavior. We present SemanticFeels, an extension of the NeuralFeels framework that integrates semantic labeling with neural implicit shape representation, from vision and touch. To illustrate its application, we focus on material classification: high-resolution Digit tactile readings are processed by a fine-tuned EfficientNet-B0 convolutional neural network (CNN) to generate local material predictions, which are then embedded into an augmented signed distance field (SDF) network that jointly predicts geometry and continuous material regions. Experimental results show that the system achieves a high correspondence between predicted and actual materials on both single- and multi-material objects, with an average matching accuracy of 79.87% across multiple manipulation trials on a multi-material object.
