Stars
Open source software for autonomous drones.
Teach-Repeat-Replan: A Complete and Robust System for Aggressive Flight in Complex Environments
A fast K Nearest Neighbor library for low-dimensional spaces
Code for the paper Dronet: Learning to Fly by Driving
[Embodied-AI-Survey-2025] Paper List and Resource Repository for Embodied AI
A RL approach to enable cost-effective, intelligent interactions between a local agent and a remote LLM
An open-source framework for collaborative AI agents, enabling diverse, distributed agents to team up and tackle complex tasks through internet-like connectivity.
[ICLR 2024] Source codes for the paper "Building Cooperative Embodied Agents Modularly with Large Language Models"
CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning (视觉-语言因果推理开源框架)
A flexible, high-performance 3D simulator for Embodied AI research.
[ICCV'21] Curious Representation Learning for Embodied Intelligence
[IROS'21] SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot Learning
Code for "MetaMorph: Learning Universal Controllers with Transformers", Gupta et al, ICLR 2022
[RSS 2021] An End-to-End Differentiable Framework for Contact-Aware Robot Design
UAV Network Communication Experimental dataset is a collection of network traffic captured from a wireless network used by unmanned aerial vehicles (UAVs) during a simulated search and rescue missi…
Drone corridors can be imagined as three dimensional highways in the sky, and are designed to support UAV operations. This dataset contains channel matrices and signal strength distribution in such…
Book Website: Dynamic System Modelling & Analysis with MATLAB & Pythobn
Reinforcement Learning on single and multi-agent quadcopters using TD3 and PPO using gym pybullet drones
Use Multi-agent Twin Delayed Deep Deterministic Policy Gradient(TD3) algorithm to find reasonable paths for ships
Code for a multi-agent particle environment used in the paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments"
Code for the RL method MATD3 described in the paper "Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics"