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.
Python module for GQ-CNN training and deployment with ROS integration.
Generative Grasping CNN from "Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach" (RSS 2018)
Baseline model for "GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping" (CVPR 2020)
Antipodal Robotic Grasping using GR-ConvNet. IROS 2020.
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.
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.
[ICRA 2022] CaTGrasp: Learning Category-Level Task-Relevant Grasping in Clutter from Simulation
Deep learning for grasp detection within MoveIt.
Deep Reinforcement Learning for Robotic Grasping from Octrees
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.
GrabNet: A Generative model to generate realistic 3D hands grasping unseen objects (ECCV2020)
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