【Motion Imitation】Robot Motion Planning Methods Learned from Human Demonstrations
A survey of research on methods for robot motion generation and planning learned from videos of humans grasping objects.
Deep Learning
32 posts
【Grasping by Imitation】Learning Robotic Grasping from Human Demonstrations
A survey of research on robotic grasping learned from videos of humans grasping objects.
[Grasp Pose Estimation Algorithm] RGB Matters Paper Notes and Reproduction
Reproducing the RGB Matters algorithm proposed by Shanghai Jiao Tong University.
[Grasping Algorithm] Contact GraspNet
Reproducing Contact GraspNet, a grasp estimation algorithm proposed by NVIDIA
[6D Pose Estimation] Gen6D Algorithm
Algorithm reproduction
GDRNPP: A 6D Pose Estimation Algorithm
Using TensorBoard | Generating Deep Learning Curves
During deep learning training, we inevitably need to observe changes in metrics such as Loss and Learning_rate, so plotting curves in real time is essential. This article explains how to use PyTorch's TensorBoard to plot curves.
[Paper Notes] 6D Pose Estimation Papers from ECCV2020–2022
Notes from searching for and reading ECCV papers related to 6D pose estimation from the past three years
[Paper Notes] Papers on 6D Pose Estimation at CVPR2020-2022
A review of and notes on CVPR papers related to 6D pose estimation from the past three years
[Paper Notes] Papers Citing DenseFusion | No Code
I searched for papers citing DenseFusion, but so many of them had no open-source code that after reading two, I switched to focusing only on papers from top-tier conferences.
[Statistical Learning Methods Notes 04] Naive Bayes
Study notes on Statistical Learning Methods
[Statistical Learning Methods Notes 03] k-Nearest Neighbors Method
The nearest neighbors algorithm assumes a training dataset is given in which the class of each instance is known. For classification, a new instance is predicted from the classes of its k nearest training instances by majority voting or similar rules. Therefore, the k-nearest neighbors method has no explicit learning process.
[Statistical Learning Methods Notes 02] Perceptron
The perceptron is a linear binary classification model. Its input is an instance's feature vector, and its output is the instance's class. Its goal is to find a separating hyperplane that linearly divides the training data.
[Statistical Learning Method Notes 01] Introduction to Statistical Learning
Statistical learning is the discipline in which computers build probabilistic and statistical models from data and use those models to predict and analyze data. These notes follow Li Hang's book *Statistical Learning Methods*.
Creating a Custom LINEMOD Dataset (ObjectDatasetTools)
I am currently studying 6D pose estimation algorithms. The LINEMOD dataset is the most commonly used dataset in this field, but testing only on an existing dataset is not enough. Building a custom dataset is necessary for practical applications.
[Deep Learning Notes 08] Classic Neural Networks
Basic implementation methods for object detection, starting with object localization and summarizing object detection algorithms such as landmark detection, object detection, the sliding-window method, and NMS (Non-Max Suppression).
[Deep Learning Notes 08] Classic Neural Networks
This article introduces several classic neural network models, including the network architectures of LeNet-5, AlexNet, VGG-16, ResNet, Inception networks, and more.
[Deep Learning Notes 07] Convolutional Neural Network
Why do neural networks often use convolution when analyzing images? Why are there padding and stride? What roles do pooling layers and fully connected layers play?
[Deep Learning Notes 06] Hyperparameter Tuning and Handling
This article introduces common parameter-selection principles and approximate ranges in neural networks, as well as the basic principles of BN and softmax.
[6D Pose Estimation Algorithm] Introduction to LineMod and Code Testing
The LineMod algorithm was proposed by Hinterstoisser et al. in 2011. Using template matching, it addresses real-time detection and 6D pose estimation and localization for low-texture three-dimensional objects in cluttered scenes.
[Deep Learning Notes 05] Common Optimization Algorithms in Deep Learning
This article introduces several of the most commonly used optimization algorithms, starting from partitioning the dataset into mini-batches, then covering momentum gradient descent, RMSprop, and their combination Adam, along with implementation details and basic principles for each method.
[Deep Learning Notes 04] Elements for Improving Training Performance and Speed
There are many ways to improve neural network training performance, such as avoiding underfitting and overfitting, using regularization, and so on. Different methods also have different effects.
[Deep Learning Notes 03] Linear Regression, Softmax Regression, and Their Loss Functions
Linear regression in deep learning, basic optimization algorithms, and implementations of Softmax regression and loss functions.
[Deep Learning Notes 02] Implementation of Data Operations and Linear Algebra Fundamentals
Introduces how to perform basic data operations with PyTorch and pandas, and how to carry out fundamental linear algebra operations and derivative computation with torch.
Deep Learning Bug List (Updated Occasionally)
When running deep learning code for training and inference, you will inevitably encounter various errors. This article collects some of the errors I have encountered and will be updated occasionally.
Classic Instance Segmentation Model: Mask R-CNN
Mask R-CNN is a major 2017 work by Kaiming He. It performs instance segmentation alongside object detection and achieves excellent results.
[Deep Learning Notes 01] Neural Networks and Deep Learning
Notes from my journey learning deep learning from scratch. This chapter introduces the most fundamental mathematical ideas behind deep learning, as well as simple regression-based approaches to deep learning algorithms and related code.
How to Understand Channels in Convolutional Neural Networks
A summary of some concepts that beginners may find difficult to understand when starting out in deep learning.
Understanding Channels in Convolutional Neural Networks
Generally, channels refers to the number of convolution kernels in each convolutional layer. The channels of the initial input image sample depend on the image type, such as RGB; after convolution, out_channels depends on the number of convolution kernels. The out_channels then also serves as the in_channels of the convolution kernel in the next convolution.
How Convolution Is Computed
Convolution is essentially the process of taking a matrix of a specific size each time, sliding it across the input matrix, and computing the inner product at each position.
Deep Learning Environment Setup with CUDA + TensorFlow 2 + Keras
The process of setting up a deep learning environment with Anaconda + CUDA + TensorFlow 2 + Keras.
Deep Learning Environment Setup with PyTorch
An installation tutorial for the commonly used deep learning environment PyTorch.