In-hand object reorientation is necessary for performing many dexterous manipulation tasks,
such as tool use in less structured environments, which remain beyond the reach of current …

In-hand object reorientation has been a challenging problem in robotics due to high
dimensional actuation space and the frequent change in contact state between the fingers …

Dexterous manipulation with a multi-finger hand is one of the most challenging problems in
robotics. While recent progress in imitation learning has largely improved the sample …

We propose to learn to generate gras** motion for manipulation with a dexterous hand
using implicit functions. With continuous time inputs, the model can generate a continuous …

Human dexterity is an invaluable capability for precise manipulation of objects in complex
tasks. The capability of robots to similarly grasp and perform in-hand manipulation of objects …

The purpose of this benchmark is to evaluate the planning and control aspects of robotic in-
hand manipulation systems. The goal is to assess the system's ability to change the pose of …

Unlike traditional robotic hands, underactuated compliant hands are challenging to model
due to inherent uncertainties. Consequently, pose estimation of a grasped object is usually …

Learning accurate representations of robot models remains a challenging problem, and is
typically approached though large, system-specific feature sets. This method inherently …

We consider the problem of in-hand dexterous manipulation with a focus on unknown or
uncertain hand–object parameters, such as hand configuration, object pose within hand …

Acquiring a precise model is a challenging task for many important robotic tasks and
systems-including in-hand manipulation using underactuated, adaptive hands. Learning …