4,257 research outputs found
Neural Grasp Distance Fields for Robot Manipulation
We formulate grasp learning as a neural field and present Neural Grasp
Distance Fields (NGDF). Here, the input is a 6D pose of a robot end effector
and output is a distance to a continuous manifold of valid grasps for an
object. In contrast to current approaches that predict a set of discrete
candidate grasps, the distance-based NGDF representation is easily interpreted
as a cost, and minimizing this cost produces a successful grasp pose. This
grasp distance cost can be incorporated directly into a trajectory optimizer
for joint optimization with other costs such as trajectory smoothness and
collision avoidance. During optimization, as the various costs are balanced and
minimized, the grasp target is allowed to smoothly vary, as the learned grasp
field is continuous. In simulation benchmarks with a Franka arm, we find that
joint grasping and planning with NGDF outperforms baselines by 63% execution
success while generalizing to unseen query poses and unseen object shapes.
Project page: https://sites.google.com/view/neural-grasp-distance-fields
Data-Driven Grasp Synthesis - A Survey
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 they synthesize grasps for known, familiar or unknown
objects. This structure allows us to identify common object representations and
perceptual processes that facilitate the employed data-driven grasp synthesis
technique. In the case of known objects, we concentrate on the approaches that
are based on object recognition and pose estimation. In the case of familiar
objects, the techniques use some form of a similarity matching to a set of
previously encountered objects. Finally for the approaches dealing with unknown
objects, the core part is the extraction of specific features that are
indicative of good grasps. Our survey provides an overview of the different
methodologies and discusses open problems in the area of robot grasping. We
also draw a parallel to the classical approaches that rely on analytic
formulations.Comment: 20 pages, 30 Figures, submitted to IEEE Transactions on Robotic
- …