论文标题
串联3D：3D对象识别的主动触觉探索
TANDEM3D: Active Tactile Exploration for 3D Object Recognition
论文作者
论文摘要
对3D对象的触觉识别仍然是一项具有挑战性的任务。与2D形状相比，3D表面的复杂几何形状需要更丰富的触觉信号，更灵活的动作和更高级的编码技术。在这项工作中，我们提出了Tandem3D，该方法将用于探索和决策的共同训练框架应用于具有触觉信号的3D对象识别。从我们以前的工作开始，该工作引入了针对2D识别问题的共同训练范式，我们引入了许多进步，使我们能够扩展到3D。 Tandem3D基于一个新颖的编码器，该编码器使用PointNet ++从触点位置和正态构建3D对象表示。此外，通过启用6DOF运动，Tandem3D以高效率探索并收集歧视性触摸信息。我们的方法完全在模拟中训练，并通过现实世界实验进行了验证。与最先进的基线相比，串联3D在识别3D对象时达到了更高的精度和较低的动作，并且也证明对不同类型和数量的传感器噪声更强大。视频可在https://jxu.ai/tandem3d上找到。
Tactile recognition of 3D objects remains a challenging task. Compared to 2D shapes, the complex geometry of 3D surfaces requires richer tactile signals, more dexterous actions, and more advanced encoding techniques. In this work, we propose TANDEM3D, a method that applies a co-training framework for exploration and decision making to 3D object recognition with tactile signals. Starting with our previous work, which introduced a co-training paradigm for 2D recognition problems, we introduce a number of advances that enable us to scale up to 3D. TANDEM3D is based on a novel encoder that builds 3D object representation from contact positions and normals using PointNet++. Furthermore, by enabling 6DOF movement, TANDEM3D explores and collects discriminative touch information with high efficiency. Our method is trained entirely in simulation and validated with real-world experiments. Compared to state-of-the-art baselines, TANDEM3D achieves higher accuracy and a lower number of actions in recognizing 3D objects and is also shown to be more robust to different types and amounts of sensor noise. Video is available at https://jxu.ai/tandem3d.
