HongminWu

BP-AR-HMM

This repository is used to discover and model dynamicl behaviors which are shared among several related time series.

HDPHMM_HDPSLDS

HDPHMM_HDPSLDS

HIRO_SA_DATA

This repo for collecting the data of HIRO robot snap assembly, which including the REAL/SIM robot in SUCCESS/FAILURE task execution.

HMM

This repo including the old version of our introspective system. its aim for testing the algorithms.

Motion-Planning-Kit

The source file was create by http://robotics.stanford.edu/~latombe/

Paper_Notes

This will contain my notes for research papers (mostly deep IL and RL).

reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

robInfLib

Kernelized Movement Primitives

SEDS

Stable Estimation of Dynamical System for Skill Learning and Realtime Collision Avoidance

time_series_anomaly_detection_classification_clustering

the state-of-the-art repo for time_series_anomaly_detection_classification_clustering

HongminWu/time_series_anomaly_detection_classification_clustering

the state-of-the-art repo for time_series_anomaly_detection_classification_clustering

HongminWu/reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

HongminWu/robInfLib

Kernelized Movement Primitives

HongminWu/aaltd18

Data augmentation using synthetic data for time series classification with deep residual networks

HongminWu/AdversarialNetsPapers

The classical paper list with code about generative adversarial nets

HongminWu/anomaly-detection-resources

Anomaly detection related books, papers, videos and toolboxes

HongminWu/awesome-anomaly-detection

A curated list of awesome anomaly detection resources

HongminWu/BIS

Benchmark on interactive safety

HongminWu/dagmm

My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

HongminWu/disentangled-representation-papers

A curated list of research papers related to learning disentangled representations

HongminWu/HongminWu.github.io

HongminWu.github.io

HongminWu/hrc_legible_motion_generation

HongminWu/hrl-assistive

PR2 controllers and interfaces for assistive teleoperation

HongminWu/Keras-GAN

Keras implementations of Generative Adversarial Networks.

HongminWu/keras_lstm_vae

Keras implementation of LSTM Variational Autoencoder

HongminWu/lantern

🔴Lantern Latest Download https://github.com/getlantern/lantern/releases/tag/latest 🔴蓝灯最新版本下载 https://github.com/getlantern/download 🔴

HongminWu/multisensory

Code for the paper: Audio-Visual Scene Analysis with Self-Supervised Multisensory Features

HongminWu/OmniAnomaly

HongminWu/papers_notes_remarkable

This repo is used to record the notes and ideas for reading a paper or debugging code, file names with the paper title commonly.

HongminWu/Peg_in_hole_assembly

Deep reinforcement learning for robotic peg-in-hole assembly task

HongminWu/RAFCON

RAFCON (RMC advanced flow control) uses hierarchical state machines, featuring concurrent state execution, to represent robot programs. It ships with a graphical user interface supporting the creation of state machines and contains IDE like debugging mechanisms. Alternatively, state machines can programmatically be generated using RAFCON's API.

HongminWu/reward-learning-rl

[RSS 2019] End-to-End Robotic Reinforcement Learning without Reward Engineering

HongminWu/RGAN

Recurrent (conditional) generative adversarial networks for generating real-valued time series data.

HongminWu/RNN-Time-series-Anomaly-Detection

RNN based Time-series Anomaly detector model implemented in Pytorch.

HongminWu/robio2018

robio2018

HongminWu/robotics-rl-srl

S-RL Toolbox: Reinforcement Learning (RL) and State Representation Learning (SRL) for Robotics

HongminWu/softqlearning

Reinforcement Learning with Deep Energy-Based Policies

HongminWu/TecNets

Official code for "Task-Embedded Control Networks for Few-Shot Imitation Learning".

HongminWu/telemanom

A framework for using LSTMs to detect anomalies in multivariate time series data. Includes spacecraft anomaly data and experiments from the Mars Science Laboratory and SMAP missions.

HongminWu/visual-pushing-grasping

Train robotic agents to learn to plan pushing and grasping actions for manipulation with deep reinforcement learning.