2 research outputs found
GraspGF: Learning Score-based Grasping Primitive for Human-assisting Dexterous Grasping
The use of anthropomorphic robotic hands for assisting individuals in
situations where human hands may be unavailable or unsuitable has gained
significant importance. In this paper, we propose a novel task called
human-assisting dexterous grasping that aims to train a policy for controlling
a robotic hand's fingers to assist users in grasping objects. Unlike
conventional dexterous grasping, this task presents a more complex challenge as
the policy needs to adapt to diverse user intentions, in addition to the
object's geometry. We address this challenge by proposing an approach
consisting of two sub-modules: a hand-object-conditional grasping primitive
called Grasping Gradient Field~(GraspGF), and a history-conditional residual
policy. GraspGF learns `how' to grasp by estimating the gradient from a success
grasping example set, while the residual policy determines `when' and at what
speed the grasping action should be executed based on the trajectory history.
Experimental results demonstrate the superiority of our proposed method
compared to baselines, highlighting the user-awareness and practicality in
real-world applications. The codes and demonstrations can be viewed at
"https://sites.google.com/view/graspgf"