Robotic Grasping
80 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
rc_49
Grasp MultiObject
Grasp-Anything
Latest papers
Inverse Kinematics for Neuro-Robotic Grasping with Humanoid Embodied Agents
We generalize the embodied agent, that was introduced for NICOL, to also be embodied by NICO.
Generalizing 6-DoF Grasp Detection via Domain Prior Knowledge
We focus on the generalization ability of the 6-DoF grasp detection method in this paper.
GaussianGrasper: 3D Language Gaussian Splatting for Open-vocabulary Robotic Grasping
In particular, we propose an Efficient Feature Distillation (EFD) module that employs contrastive learning to efficiently and accurately distill language embeddings derived from foundational models.
STAR: Shape-focused Texture Agnostic Representations for Improved Object Detection and 6D Pose Estimation
To achieve a focus on learning shape features, the textures are randomized during the rendering of the training data.
PGA: Personalizing Grasping Agents with Single Human-Robot Interaction
JHKim-snu/PGA
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Based on the acquired information, PGA pseudo-labels objects in the Reminiscence by our proposed label propagation algorithm.
Quality Diversity through Human Feedback
ld-ing/qdhf
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Meanwhile, Quality Diversity (QD) algorithms excel at identifying diverse and high-quality solutions but often rely on manually crafted diversity metrics.
Domain Randomization for Sim2real Transfer of Automatically Generated Grasping Datasets
More than 7000 reach-and-grasp trajectories have been generated with Quality-Diversity (QD) methods on 3 different arms and grippers, including parallel fingers and a dexterous hand, and tested in the real world.
Toward a Plug-and-Play Vision-Based Grasping Module for Robotics
This framework addresses two main issues: the lack of an off-the-shelf vision module for detecting object pose and the generalization of QD trajectories to the whole robot operational space.
Grasp-Anything: Large-scale Grasp Dataset from Foundation Models
Foundation models such as ChatGPT have made significant strides in robotic tasks due to their universal representation of real-world domains.
SCENEREPLICA: Benchmarking Real-World Robot Manipulation by Creating Replicable Scenes
We present a new reproducible benchmark for evaluating robot manipulation in the real world, specifically focusing on pick-and-place.
