mando1106 Goto Github PK

act-plus-plus

Imitation Learning algorithms with Co-traing for Mobile ALOHA: ACT, Diffusion Policy, VINN

beamformopt

传感器阵列波束优化设计与应用部分章节程序

catalyst-rl-tutorial

Using Catalyst.RL to train a robot to perform peg-in-hole insertion in simulation.

deep-reinforcement-learning-algorithms-with-pytorch

Clean, Robust, and Unified PyTorch implementation of popular DRL Algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)

diploma-thesis

Source code of my diploma-thesis project on closed loop system identification via RBF neural networks.

drlib

DRLib：A concise deep reinforcement learning library, integrating HER and PER for almost off policy RL algos.

dynamic_calibration

Identification of dynamic parameters of robotic systems

dynamic_parameter_identification_for_rokae_xmate

Dynamic parameter identification code for rokae xmate manipulator based on MATLAB, including excitation trajectory optimization, LSM method, and N-E formulation of dynamic equation.

learningnote

C++和Linux学习笔记

mando_rl

记录code

modernrobotics

Modern Robotics: Mechanics, Planning, and Control Code Library --- The primary purpose of the provided software is to be easy to read and educational, reinforcing the concepts in the book. The code is optimized neither for efficiency nor robustness.

peg-in-hole

peg-in-hole-with-rl

pytorch-ident

System identification in PyTorch

qm_dbk

Exploring variational methods in quantum mechanics using Daubechies' wavelets in MATLAB

rher

The official code for paper “Relay Hindsight Experience Replay: Self-Guided Continual Reinforcement Learning for Sequential Object Manipulation Tasks with Sparse Rewards”

rnn-adaptation

Code accompanying the paper "On the adaptation of recurrent neural networks for system identification"

rob_dyn

some-useful-kill

sysid-neural-continuous

Continuous-time system identification with neural networks

thesis-example

Αn online identification experiment of a 2-DOF robotic manipulator

timeawarernn

Code used for the AAAI 2020 paper "System Identification with Time-Aware Neural Sequence Models"