TANDEM3D: Active Tactile Exploration for 3D Object Recognition

• URL: http://arxiv.org/abs/2209.08772v1
• Date: Mon, 19 Sep 2022 05:54:26 GMT
• Title: TANDEM3D: Active Tactile Exploration for 3D Object Recognition
• Authors: Jingxi Xu, Han Lin, Shuran Song, Matei Ciocarlie
• Abstract summary: We propose TANDEM3D, a method that applies a co-training framework for 3D object recognition with tactile signals. TANDEM3D is based on a novel encoder that builds 3D object representation from contact positions and normals using PointNet++. Our method is trained entirely in simulation and validated with real-world experiments.
• Score: 16.548376556543015
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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