The inference module for AgiBot X1.

The hardware design for AgiBot X1.

The reinforcement learning training code for AgiBot X1.

Web labeling tool for bitmap images and point clouds

Code comment for 3DGS

为GPT/GLM等LLM大语言模型提供实用化交互接口，特别优化论文阅读/润色/写作体验，模块化设计，支持自定义快捷按钮&函数插件，支持Python和C++等项目剖析&自译解功能，PDF/LaTex论文翻译&总结功能，支持并行问询多种LLM模型，支持chatglm3等本地模型。接入通义千问, deepseekcoder, 讯飞星火, 文心一言, llama2, rwkv, claude2, m…

Robotics Toolbox for Python

Open3D: A Modern Library for 3D Data Processing

Python Library for MuJoCo Format model xml

Detects when CPU/GPU is idle and sends a notification to phone

Certifiable Outlier-Robust Geometric Perception

Papers and Datasets about Point Cloud.

BAT程序员自己的简历模板分享出来了 。技术简历追求简单明了，避免没有必要的花哨修饰，大家可以fork到自己仓库中，基于这个模板进行修改。

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo.

强化学习中文教程，在线阅读地址：https://datawhalechina.github.io/easy-rl/

PyTorch implementation of Hierarchical Actor Critic (HAC) for OpenAI gym environments

A toolkit for developing and comparing reinforcement learning algorithms.

[CVPR 2021] SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

AAPM DL Sparse-View Computed Tomography Image Reconstruction Challenge - Team Robust-And-Stable

MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.

Robotics Gym Environments

Dual UR5 Husky Robot MuJoCo Model

A MuJoCo/Gym environment for robot control using Reinforcement Learning. The task of agents in this environment is pixel-wise prediction of grasp success chances.

Implement many Sparse Reward algorithms in Gym Fetch environment

This repository contains the code to implement the Hierarchical Actor-Critic (HAC) algorithm.

Computed Tomography Image Reconstruction Project using MATLAB

Training networks to compute forward and inverse kinematics - more generally, nonlinear function estimation with potential singularities