Gras** is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

The Advancements in tactile sensors and machine learning techniques open new
opportunities for achieving intelligent gras** in robotics. Traditional robot is limited in its …

Multi-fingered hands could be used to achieve many dexterous manipulation tasks, similarly
to humans, and tactile sensing could enhance the manipulation stability for a variety of …

Robot simulation has been an essential tool for data-driven manipulation tasks. However,
most existing simulation frameworks lack either efficient and accurate models of physical …

Tactile sensing of skin shapes the interactions between hand and the surrounding world,
owing to the remarkable natural sensory system. But for astronauts, tactile feedback cannot …

Reliably planning fingertip grasps for multi-fingered hands lies as a key challenge for many
tasks including tool use, insertion, and dexterous in-hand manipulation. This task becomes …

Efficient and accurate 3D object shape reconstruction contributes significantly to the success
of a robot's physical interaction with its environment. Acquiring accurate shape information …

In robotics, active exploration and learning in uncertain environments must take into account
safety, as the robot may otherwise damage itself or its surroundings. This paper presents a …

Generalizable manipulation requires that robots be able to interact with novel objects and
environment. This requirement makes manipulation extremely challenging as a robot has to …

In this letter, we address the problem of task-informed gras** in scenarios where only
incomplete or partial object information is available. Existing methods, which either focus on …