ViTac-Tracing: 変形物体のトレーシングのための視覚-触覚模倣学習
ViTac-Tracing: Visual-Tactile Imitation Learning of Deformable Object Tracing
変形物体を1次元・2次元でトレースする統一モデルを、視覚と触覚の情報を組み合わせた模倣学習で実現した論文。局所的な触覚中心の接触維持と大域的なタスク進行の損失を導入し、低コスト遠隔操作システムに触覚を統合して汎用性と実世界での信頼性を向上させた。
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: Yongqiang Zhao, Haining Luo, Yupeng Wang, Emmanouil Spyrakos Papastavridis, Yiannis Demiris, Shan Luo
分類: cs.RO
原文アブストラクト
Deformable objects often appear in unstructured configurations. Tracing deformable objects helps bringing them into extended states and facilitating the downstream manipulation tasks. Due to the requirements for object-specific modeling or sim-to-real transfer, existing tracing methods either lack generalizability across different categories of deformable objects or struggle to complete tasks reliably in the real world. To address this, we propose a novel visual-tactile imitation learning method to achieve one-dimensional (1D) and two-dimensional (2D) deformable object tracing with a unified model. Our method is designed from both local and global perspectives based on visual and tactile sensing. Locally, we introduce a weighted loss that emphasizes actions maintaining contact near the center of the tactile image, improving fine-grained adjustment. Globally, we propose a tracing task loss that helps the policy to regulate task progression. On the hardware side, to compensate for the limited features extracted from visual information, we integrate tactile sensing into a low-cost teleoperation system considering both the teleoperator and the robot. Extensive ablation and comparative experiments on diverse 1D and 2D deformable objects demonstrate the effectiveness of our approach, achieving an average success rate of 80% on seen objects and 65% on unseen objects.
