Pinned Repositories
awesome-cpp
A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.
awesome-grasping
A curated list of awesome grasping libraries and resources
awesome-robotics
A list of awesome Robotics resources
awesome-ros
😎 A curated list of awesome robotics resources based on ROS
awesome-semantic-segmentation
:metal: awesome-semantic-segmentation
Best-README-Template
An awesome README template to jumpstart your projects!
clash_for_windows
Fndroid/clash_for_windows_pkg/releases 最后备份文件
DE3-ROB1-CHESS
CHESS Project for DE3 Robotics 1 (March 2018)
Deep-Box-Packing
The Packing problem has gained much relevance with the recent upheaval of the delivery and retail industry. Companies all over the world are now subject to massive logistics & operations schemes, and their warehouses‘ e ectiveness is irrevocably bound to how well their products are packed into trucks for distribution. Optimizing this process may lead to huge improvements in performance, time use, resource management and to ultimately increasing profits. Seeking to perform and deliver this optimization, this work proposes a new method called “Deep Box Packing” (DBP), an online system which is able to provide an optimized packing strategy for an arbitrary set of three-dimensional boxes arriving in real-time. DBP was trained using Deep Reinforcement Learning and leverages the power of attention mechanisms in a modified version of the Transformer Network called here the Mapping Transformer. It was conceived to work under partial information, in real-time, and to respond to all of the three inherent questions of packing: which box to take (selection), where to place it in the container (position) and how to place it (orientation) at every given moment in time. Its reward function was tailored not only in terms of optimizing the final Volume utilization of the container but also in terms of the feasibility of the packing sequence, withholding constraints such as box stability and accessibility to the packing positions from the entrance of the container. Under this scenario, DBP was capable of achieving outstanding results in the tested instances up to 100% volume utilization in fully feasible packings. Under comparative tests, DBP considerably improved results obtained from a wall-building LB-Greedy heuristic and showed high generalization capacity to different sizes of the Information window (number of boxes from the whole sequence it can see and choose from at any moment in time). After a set of visual step-by-step analyses of DBP’s behavior in generated packing sequences, it was also shown that it was able to achieve high geometric understanding and great potential for being expanded into a real warehouse scenario.
GOPT
The code is under preparation and is coming soon.
Xiong5Heng's Repositories
Xiong5Heng/awesome-ros
😎 A curated list of awesome robotics resources based on ROS
Xiong5Heng/GOPT
The code is under preparation and is coming soon.
Xiong5Heng/awesome-cpp
A curated list of awesome C++ (or C) frameworks, libraries, resources, and shiny things. Inspired by awesome-... stuff.
Xiong5Heng/awesome-grasping
A curated list of awesome grasping libraries and resources
Xiong5Heng/awesome-robotics
A list of awesome Robotics resources
Xiong5Heng/awesome-semantic-segmentation
:metal: awesome-semantic-segmentation
Xiong5Heng/Best-README-Template
An awesome README template to jumpstart your projects!
Xiong5Heng/clash_for_windows
Fndroid/clash_for_windows_pkg/releases 最后备份文件
Xiong5Heng/DE3-ROB1-CHESS
CHESS Project for DE3 Robotics 1 (March 2018)
Xiong5Heng/Deep-Box-Packing
The Packing problem has gained much relevance with the recent upheaval of the delivery and retail industry. Companies all over the world are now subject to massive logistics & operations schemes, and their warehouses‘ e ectiveness is irrevocably bound to how well their products are packed into trucks for distribution. Optimizing this process may lead to huge improvements in performance, time use, resource management and to ultimately increasing profits. Seeking to perform and deliver this optimization, this work proposes a new method called “Deep Box Packing” (DBP), an online system which is able to provide an optimized packing strategy for an arbitrary set of three-dimensional boxes arriving in real-time. DBP was trained using Deep Reinforcement Learning and leverages the power of attention mechanisms in a modified version of the Transformer Network called here the Mapping Transformer. It was conceived to work under partial information, in real-time, and to respond to all of the three inherent questions of packing: which box to take (selection), where to place it in the container (position) and how to place it (orientation) at every given moment in time. Its reward function was tailored not only in terms of optimizing the final Volume utilization of the container but also in terms of the feasibility of the packing sequence, withholding constraints such as box stability and accessibility to the packing positions from the entrance of the container. Under this scenario, DBP was capable of achieving outstanding results in the tested instances up to 100% volume utilization in fully feasible packings. Under comparative tests, DBP considerably improved results obtained from a wall-building LB-Greedy heuristic and showed high generalization capacity to different sizes of the Information window (number of boxes from the whole sequence it can see and choose from at any moment in time). After a set of visual step-by-step analyses of DBP’s behavior in generated packing sequences, it was also shown that it was able to achieve high geometric understanding and great potential for being expanded into a real warehouse scenario.
Xiong5Heng/Deep-Reinforcement-Learning-Hands-On
Hands-on Deep Reinforcement Learning, published by Packt
Xiong5Heng/franka_interactive_controllers
Control interface built on top of franka_ros that allows controlling the franka robot arm in several joint and Cartesian space impedance control schemes for interactive, safe and reactive (mostly DS-based) motion planning and learning. This low-level control interface is used and developed by/for Prof. Nadia Figueroa and her collaborators/students.
Xiong5Heng/Franka_panda-usage-example
Xiong5Heng/frankx
High-Level Motion Library for the Franka Panda Robot
Xiong5Heng/learning-shifting-for-grasping
Robot Learning of Shifting Objects for Grasping in Cluttered Environments
Xiong5Heng/machine-learning-yearning-cn
Machine Learning Yearning 中文版 - 《机器学习训练秘籍》 - Andrew Ng 著
Xiong5Heng/or_parabolicsmoother
An OpenRAVE Plugin for Parabolic Smoothing
Xiong5Heng/pybullet-blender-recorder
Xiong5Heng/PythonRobotics
Python sample codes for robotics algorithms.
Xiong5Heng/rl
The Robotics Library (RL) is a self-contained C++ library for rigid body kinematics and dynamics, motion planning, and control.
Xiong5Heng/TOPP
Time-Optimal Path Parameterization (à la Bobrow)
Xiong5Heng/USTC-Course
:heart:**科学技术大学课程资源
Xiong5Heng/v-hacd
Automatically exported from code.google.com/p/v-hacd
Xiong5Heng/vision-based-robotic-grasping
Related papers and codes for vision-based robotic grasping