Tactile exploration plays a crucial role in understanding object structures
for fundamental robotics tasks such as grasping and manipulation. However,
efficiently exploring such objects using tactile sensors is challenging,
primarily due to the large-scale unknown environments and limited sensing
coverage of these sensors. To this end, we present AcTExplore, an active
tactile exploration method driven by reinforcement learning for object
reconstruction at scales that automatically explores the object surfaces in a
limited number of steps. Through sufficient exploration, our algorithm
incrementally collects tactile data and reconstructs 3D shapes of the objects
as well, which can serve as a representation for higher-level downstream tasks.
Our method achieves an average of 95.97% IoU coverage on unseen YCB objects
while just being trained on primitive shapes.

利用强化学习驱动的主动触觉探索方法，通过有限步骤对物体进行重新构建，从而更好地理解物体结构并获取三维形状的表示，为机器人抓取和操作等基础任务提供有效的解决方案。

AcTExplore：未知物体上的主动触觉探索

AcTExplore: Active Tactile Exploration on Unknown Objects

Deep neural networks can model images with rich latent representations, but
they cannot naturally conceptualize structures of object categories in a
human-perceptible way. This paper addresses the problem of learning object
structures in an image modeling process without supervision. We propose an
autoencoding formulation to discover landmarks as explicit structural
representations. The encoding module outputs landmark coordinates, whose
validity is ensured by constraints that reflect the necessary properties for
landmarks. The decoding module takes the landmarks as a part of the learnable
input representations in an end-to-end differentiable framework. Our discovered
landmarks are semantically meaningful and more predictive of manually annotated
landmarks than those discovered by previous methods. The coordinates of our
landmarks are also complementary features to pretrained deep-neural-network
representations in recognizing visual attributes. In addition, the proposed
method naturally creates an unsupervised, perceptible interface to manipulate
object shapes and decode images with controllable structures. The project
webpage is at this http URL

本文提出一种基于自动编码器的方法，无需监督信号，从而学习图像模型中的对象结构，发现了与手动注释地标更为相关的语义有意义的地标，这些地标是识别视觉属性的补充特征，并且自然地创建了一个无监督、可感知的界面，用于操纵对象形状和解码具有可控结构的图像。