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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.

Supplementary Video (2min20sec)

Individual Real-world Demonstrations

These real-world demonstrations of each object correspond to examples of Figure 5 in the paper. Notable behaviors:

Object 0 (Bleach Cleanser)

Object 1 (Bowl)

Object 2 (Cracker Box)

Object 3 (Master Chef Can)

Object 4 (Mustard Bottle)

Object 5 (Pitcher Base)

Object 6 (Potted Meat Can)

Object 7 (Power Drill)

Object 8 (Sugar Box)

Object 9 (Tomato Soup Can)


  title={TANDEM3D: Active Tactile Exploration for 3D Object Recognition},
  author={Xu, Jingxi and Lin, Han and Song, Shuran and Ciocarlie, Matei},
  journal={arXiv preprint arXiv:2209.08772},