Grasping 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 motivation behind our work is to review and analyze the most relevant studies on deep
reinforcement learning-based object manipulation. Various studies are examined through a …

In this letter, a novel grasp detection neural network Squeeze-and-Excitation ResUNet (SE-
ResUNet) is developed, where the residual block with the channel attention is integrated …

Grasping with anthropomorphic robotic hands involves much more hand-object interactions
compared to parallel-jaw grippers. Modeling hand-object interactions is essential to the …

Recent advances in multi-fingered robotic grasping have enabled fast 6-Degrees-of-
Freedom (DOF) single object grasping. Multi-finger grasping in cluttered scenes, on the …

While there exists many methods for manipulating rigid objects with parallel-jaw grippers,
grasping with multi-finger robotic hands remains a quite unexplored research topic …

This letter addresses the problem of simultaneously exploring an unknown object to model
its shape, using tactile sensors on robotic fingers, while also improving finger placement to …

If robots could reliably manipulate the shape of 3D deformable objects, they could find
applications in fields ranging from home care to warehouse fulfillment to surgical assistance …

Robotic grasping in highly cluttered environments remains a challenging task due to the lack
of collision free grasp affordances. In such conditions, non-prehensile actions could help to …

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 …