Deep learning approaches to grasp synthesis: A review
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 …
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review
This paper presents a comprehensive survey on vision-based robotic grasping. We
conclude three key tasks during vision-based robotic grasping, which are object localization …
conclude three key tasks during vision-based robotic grasping, which are object localization …
Neural descriptor fields: Se (3)-equivariant object representations for manipulation
We present Neural Descriptor Fields (NDFs), an object representation that encodes both
points and relative poses between an object and a target (such as a robot gripper or a rack …
points and relative poses between an object and a target (such as a robot gripper or a rack …
Partmanip: Learning cross-category generalizable part manipulation policy from point cloud observations
Learning a generalizable object manipulation policy is vital for an embodied agent to work in
complex real-world scenes. Parts, as the shared components in different object categories …
complex real-world scenes. Parts, as the shared components in different object categories …
Se (3)-equivariant relational rearrangement with neural descriptor fields
We present a framework for specifying tasks involving spatial relations between objects
using only 5-10 demonstrations and then executing such tasks given point cloud …
using only 5-10 demonstrations and then executing such tasks given point cloud …
Dexart: Benchmarking generalizable dexterous manipulation with articulated objects
To enable general-purpose robots, we will require the robot to operate daily articulated
objects as humans do. Current robot manipulation has heavily relied on using a parallel …
objects as humans do. Current robot manipulation has heavily relied on using a parallel …
kpam 2.0: Feedback control for category-level robotic manipulation
In this letter, we explore generalizable, perception-to-action robotic manipulation for precise,
contact-rich tasks. In particular, we contribute a framework for closed-loop robotic …
contact-rich tasks. In particular, we contribute a framework for closed-loop robotic …
Keypointdeformer: Unsupervised 3d keypoint discovery for shape control
We introduce KeypointDeformer, a novel unsupervised method for shape control through
automatically discovered 3D keypoints. We cast this as the problem of aligning a source 3D …
automatically discovered 3D keypoints. We cast this as the problem of aligning a source 3D …
DFields: Dynamic 3D Descriptor Fields for Zero-Shot Generalizable Robotic Manipulation
Scene representation has been a crucial design choice in robotic manipulation systems. An
ideal representation should be 3D, dynamic, and semantic to meet the demands of diverse …
ideal representation should be 3D, dynamic, and semantic to meet the demands of diverse …
Unsupervised learning of visual 3d keypoints for control
Learning sensorimotor control policies from high-dimensional images crucially relies on the
quality of the underlying visual representations. Prior works show that structured latent …
quality of the underlying visual representations. Prior works show that structured latent …