11 accepted papers
Perceiving the physical properties of different surfaces/textures via tactile sensing has been a long-standing problem in robotics. Most prior work has been limited to discriminative models that classify textures into a fixed set of categories. However, to enable seamless autonomous manipulation, ro…
Perceiving the physical properties of different surfaces/textures via tactile sensing has been a long-standing problem in robotics. Most prior work has been limited to discriminative models that classify textures into a fixed set of categories. However, to enable seamless and efficient autonomous ma
An essential problem in robotic systems that are to be deployed in unstructured environments is the accurate and autonomous perception of the shapes of previously unseen objects. Existing methods for shape estimation or reconstruction have leveraged either visual or tactile interactive exploration t
Point cloud registration is a fundamental and challenging problem for autonomous robots interacting in unstructured environments for applications such as object pose estimation, simultaneous localization and mapping, robot-sensor calibration, and so on. In global correspondence-based point cloud reg…
For robotic systems to interact with objects in dynamic environments, it is essential to perceive the physical properties of the objects such as shape, friction coefficient, mass, center of mass, and inertia. This not only eases selecting manipulation action but also ensures the task is performed as…
Accurate shape reconstruction of transparent ob-jects is a challenging task due to their non-Lambertian surfaces and yet necessary for robots for accurate pose perception and safe manipulation. As vision-based sensing can produce erroneous measurements for transparent objects, the tactile modality i…
This work presents a novel active visuo-tactile based framework for robotic systems to accurately estimate pose of objects in dense cluttered environments. The scene representation is derived using a novel declutter graph (DG) which describes the relationship among objects in the scene for declutter
We propose for the first time, a novel deep active visuo-tactile cross-modal full-fledged framework for object recognition by autonomous robotic systems. Our proposed network <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">xAVTNet</i> is actively tra
This paper proposes a novel active visuo-tactile based methodology wherein the accurate estimation of the time-invariant SE(3) pose of objects is considered for autonomous robotic manipulators. The robot equipped with tactile sensors on the gripper is guided by a vision estimate to actively explore…
In this letter, we propose a complete probabilistic tactile-based framework to enable robots to autonomously explore unknown workspaces and recognize objects based on their physical properties. Our framework consists of three components: 1) an active pretouch strategy to efficiently explore unknown
This paper proposes an online tactile transfer learning strategy for discriminating objects through the surface texture properties via a robotic hand and an artificial robotic skin. The proposed method has the ability to autonomously select and exploit the previously learned multiple texture models