Tactile sensing is an essential element of autonomous dexterous robot hand manipulation. It
provides information about forces of interaction and surface properties at points of contact …

The Cloud infrastructure and its extensive set of Internet-accessible resources has potential
to provide significant benefits to robots and automation systems. We consider robots and …

To reduce data collection time for deep learning of robust robotic grasp plans, we explore
training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp …

For humans, the process of grasping an object relies heavily on rich tactile feedback. Most
recent robotic grasping work, however, has been based only on visual input, and thus …

Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires
both reasoning about unseen object parts and potential collisions with the manipulator. Most …

We review the work on data-driven grasp synthesis and the methodologies for sampling and
ranking candidate grasps. We divide the approaches into three groups based on whether …

This paper presents the Dexterity Network (Dex-Net) 1.0, a dataset of 3D object models and
a sampling-based planning algorithm to explore how Cloud Robotics can be used for robust …

We extend the computation and information sharing capabilities of networked robotics by
proposing a cloud robotic architecture. The cloud robotic architecture leverages the …

A successful grasp requires careful balancing of the contact forces. Deducing whether a
particular grasp will be successful from indirect measurements, such as vision, is therefore …

With the development of cloud computing, big data, and other emerging technologies, the
integration of cloud technology and multi-robot systems makes it possible to design multi …