Train robotic agents to learn to plan pushing and grasping actions for manipulation with deep reinforcement learning.
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Updated
May 11, 2021 - Python
Train robotic agents to learn to plan pushing and grasping actions for manipulation with deep reinforcement learning.
Summary of key papers and blogs about diffusion models to learn about the topic. Detailed list of all published diffusion robotics papers.
A curated list of 3D Vision papers relating to Robotics domain in the era of large models i.e. LLMs/VLMs, inspired by awesome-computer-vision, including papers, codes, and related websites
Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020)
Generative Grasping CNN from "Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach" (RSS 2018)
Antipodal Robotic Grasping using GR-ConvNet. IROS 2020.
Deep Reinforcement Learning for Robotic Grasping from Octrees
Python module for GQ-CNN training and deployment with ROS integration.
[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
MIT-Princeton Vision Toolbox for Robotic Pick-and-Place at the Amazon Robotics Challenge 2017 - Robotic Grasping and One-shot Recognition of Novel Objects with Deep Learning.
Pytorch implementation of diffusion models on Lie Groups for 6D grasp pose generation https://sites.google.com/view/se3dif/home
paper list of robotic grasping and some related works
GRAB: A Dataset of Whole-Body Human Grasping of Objects
Detecting robot grasping positions with deep neural networks. The model is trained on Cornell Grasping Dataset. This is an implementation mainly based on the paper 'Real-Time Grasp Detection Using Convolutional Neural Networks' from Redmon and Angelova.
GrabNet: A Generative model to generate realistic 3D hands grasping unseen objects (ECCV2020)
Toolbox for our GraspNet-1Billion dataset.
ROS2.0 Foxy and Humble repositories which provide ready-to-use ROS2.0 Gazebo + MoveIt!2 simulation packages for different Industrial and Collaborative Robots.
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