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acados

Fast and embedded solvers for nonlinear optimal control

aerobenchvvpython

Python Version of F-16 Maneuver Verification Benchmark

another_drone_sim

carnd-advanced-lane-lines

carnd-behavioral-cloning-p3

Starting files for the Udacity CarND Behavioral Cloning Project

carnd-extended-kalman-filter-project

Self-Driving Car Nanodegree Program Starter Code for the Extended Kalman Filter Project

carnd-lanelines-p1

Lane Finding Project for Self-Driving Car ND

carnd-traffic-sign-classifier-project

Classify Traffic Signs.

cbf-rrt

gym-pybullet-drones

PyBullet Gym environments for single and multi-agent reinforcement learning of quadcopter control

gym-webots-drone

Webots scene gym environment for drone navigation tasks methods

lqr_cbf_rrtstar

macbf

Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificates

neural_clbf

Toolkit for learning controllers based on robust control Lyapunov barrier functions

neuromancer

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

quadrotor-simulation

Trajectory Planning and control

rotorpy

A multirotor simulator with aerodynamics for education and research.

rpg_time_optimal

Time-Optimal Planning for Quadrotor Waypoint Flight

self-triggered-control

vectorizedmultiagentsimulator

VMAS is a vectorized framework designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.

webotsmultiagentenv

This is an Webots environment with omnidirectional-robots for testing algorithms