Humans rely on touch and tactile sensing for a lot of dexterous manipulation
tasks. Our tactile sensing provides us with a lot of information regarding
contact formations as well as geometric information about objects during any
interaction. With this motivation, vision-based tactile sensors are being
widely used for various robotic perception and control tasks. In this paper, we
present a method for interactive perception using vision-based tactile sensors
for multi-object assembly. In particular, we are interested in tactile
perception during part mating, where a robot can use tactile sensors and a
feedback mechanism using particle filter to incrementally improve its estimate
of objects that fit together for assembly. To do this, we first train a deep
neural network that makes use of tactile images to predict the probabilistic
correspondence between arbitrarily shaped objects that fit together. The
trained model is used to design a particle filter which is used twofold. First,
given one partial (or non-unique) observation of the hole, it incrementally
improves the estimate of the correct peg by sampling more tactile observations.
Second, it selects the next action for the robot to sample the next touch (and
thus image) which results in maximum uncertainty reduction to minimize the
number of interactions during the perception task. We evaluate our method on
several part-mating tasks for assembly using a robot equipped with a
vision-based tactile sensor. We also show the efficiency of the proposed action
selection method against a naive method. See supplementary video at
this https URL .

本文章提出了一种使用基于视觉的触觉传感器进行交互式感知的方法，其中训练了一个深度神经网络来预测任意形状的零件的概率对应关系，并设计了一个粒子滤波器来优化感知任务并提高机器人操作的效率。