Robotic Grasping
79 papers with code • 4 benchmarks • 16 datasets
This task is composed of using Deep Learning to identify how best to grasp objects using robotic arms in different scenarios. This is a very complex task as it might involve dynamic environments and objects unknown to the network.
Benchmarks
These leaderboards are used to track progress in Robotic Grasping
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Libraries
Use these libraries to find Robotic Grasping models and implementationsDatasets
Cornell
EGAD
Dex-Net 2.0
GraspNet-1Billion
CONG
NBMOD
Grasp MultiObject
Grasp-Anything
Latest papers with no code
Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping
A crucial practical aspect for an object identification model is to be flexible in input size.
You Only Scan Once: A Dynamic Scene Reconstruction Pipeline for 6-DoF Robotic Grasping of Novel Objects
In the realm of robotic grasping, achieving accurate and reliable interactions with the environment is a pivotal challenge.
SAID-NeRF: Segmentation-AIDed NeRF for Depth Completion of Transparent Objects
Acquiring accurate depth information of transparent objects using off-the-shelf RGB-D cameras is a well-known challenge in Computer Vision and Robotics.
Speeding up 6-DoF Grasp Sampling with Quality-Diversity
We believe these results to be a significant step toward the generation of large datasets that can lead to robust and generalizing robotic grasping policies.
Grasping Trajectory Optimization with Point Clouds
The task space of a robot is represented by a point cloud that can be obtained from depth sensors.
PhyGrasp: Generalizing Robotic Grasping with Physics-informed Large Multimodal Models
With these two capabilities, PhyGrasp is able to accurately assess the physical properties of object parts and determine optimal grasping poses.
Jacquard V2: Refining Datasets using the Human In the Loop Data Correction Method
The enhanced dataset, named the Jacquard V2 Grasping Dataset, served as the training data for a range of neural networks.
Robust Analysis of Multi-Task Learning on a Complex Vision System
(2) We empirically compare the method performance when applied on feature-level gradients versus parameter-level gradients over a large set of MTL optimization algorithms, and conclude that this feature-level gradients surrogate is reasonable when there are method-specific theoretical guarantee but not generalizable to all methods.
Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact
We also incorporate cross-attention mechanisms to capture the interplay between the objects.
AGILE: Approach-based Grasp Inference Learned from Element Decomposition
The proposed method acquires a 90% grasp success rate on seen objects and 78% on unseen objects in the Coppeliasim simulation environment.
