Learning multiple gaits of quadruped robot using hierarchical reinforcement learning

HyperDog is a quadruped robot which is fully based on ROS 2 and Micro-ROS

This repository contains all the code and files needed to simulate the notspot quadrupedal robot using Gazebo and ROS.

Project Page for Lifelike Agility and Play in Quadrupedal Robots using Reinforcement Learning and Generative Pre-trained Models

Deep RL for MPC control of Quadruped Robot Locomotion

OpenAI Gym environments for an open-source quadruped robot (SpotMicro)

LLM guides robot operations 🦾💡

大模型具身智能比赛-机器人控制端

Robot Formation Navigation in Gazebo with Deep Reinforcement Learning.

Mamba SSM architecture

Use Multi-Agent Deep Deterministic Policy Gradient(DDPG) algorithm to find reasonable paths for ships

Use Multi-agent Twin Delayed Deep Deterministic Policy Gradient(TD3) algorithm to find reasonable paths for ships

Minimalistic multi-agent simulation with three agents moving in a formation

Vein-based Coalition for Multi-Agent Formation Task

Graphical software application for simulating automatic control laws that allow a multi-agents system of robots to reach desired formations in the 3D space and compare them to establish benefits an…

Multi-Agent in formation Training Environment for Reinforcement learning

multi-agent formation control environment implemented with MPE.

Multi agent scalable reinforcement learning for formation control with collision avoidance

🛸 An implementation of multi-agent flocking formation control with specific formations that can follow a target without collision and can avoid obstacles.

Formation control for a multi-agent system in a bidimensional space. Various types of formations available. Implementation in ROS2 Foxy.

The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement …

A simple example of how to implement vector based DDPG for MARL tasks using PyTorch and a ML-Agents environment.

Paper list of multi-agent reinforcement learning (MARL)

Unity Environment: Tennis

Dream to Control: Learning Behaviors by Latent Imagination

This is a DRL(Deep Reinforcement Learning) platform built with Gazebo for the purpose of robot's adaptive path planning.

ROS package which uses the Navigation Stack to autonomously explore an unknown environment with help of GMAPPING and constructs a map of the explored environment. Finally, a path planning algorithm…

Autonomous Navigation of UAV using Reinforcement Learning algorithms.

Sampling-based Planning Algorithms with Constraint for a 7-DoF Robot Arm. The algorithms include RRT, CBiRRT, PRM, and OBPRM.