触覚センシングによる把持物体の汎化可能な6D姿勢追跡の強化
Enhancing Generalizable 6D Pose Tracking of an In-Hand Object with Tactile Sensing
触覚信号を活用して、未知の把持物体の6D姿勢追跡を改善するTEG-Trackを提案し、実世界データセットを構築して有効性を示した。
著者: Yun Liu, Xiaomeng Xu, Weihang Chen, Haocheng Yuan, He Wang, Jing Xu, Rui Chen, Li Yi
分類: cs.CV, cs.RO
原文アブストラクト
When manipulating an object to accomplish complex tasks, humans rely on both vision and touch to keep track of the object's 6D pose. However, most existing object pose tracking systems in robotics rely exclusively on visual signals, which hinder a robot's ability to manipulate objects effectively. To address this limitation, we introduce TEG-Track, a tactile-enhanced 6D pose tracking system that can track previously unseen objects held in hand. From consecutive tactile signals, TEG-Track optimizes object velocities from marker flows when slippage does not occur, or regresses velocities using a slippage estimation network when slippage is detected. The estimated object velocities are integrated into a geometric-kinematic optimization scheme to enhance existing visual pose trackers. To evaluate our method and to facilitate future research, we construct a real-world dataset for visual-tactile in-hand object pose tracking. Experimental results demonstrate that TEG-Track consistently enhances state-of-the-art generalizable 6D pose trackers in synthetic and real-world scenarios. Our code and dataset are available at https://github.com/leolyliu/TEG-Track.
