User Tools



An Open-Source C++ Library for Robotics and Optimal Control

The ADRL Control Toolbox is a C++ library for efficient modelling, control and estimation for robotics.

The source code is available at https://bitbucket.org/adrlab/ct

Link to the documentation: https://adrlab.bitbucket.io/ct

Xpp: A ROS-framework to visualize floating base robots

Xpp is a collection of ROS-packages (http://wiki.ros.org/xpp) for the visualization of motion plans for floating-base robots. Apart from drawing support areas, contact forces and motion trajectories in RVIZ, it also displays these plans for specific robots. Current robots include a one-legged, a two-legged hopper, HyQ and a quadrotor.

Source https://github.com/leggedrobotics/xpp

RCARS: Robot-Centric Absolute Reference System

RCARS (Robot-Centric Absolute Reference System) is a ROS Metapackage that provides a lightweight and easy to use, visual inertial state estimation and/or motion capture system. It uses a Simultaneous Localization And Mapping (SLAM) approach based on aritificual landmarks (“fiducials”) observed by a camera and inertial measurement data retrieved from an IMU. Yet, the system is still fast and easily integratable into existing systems.

Link to the source code:https://bitbucket.org/adrlab/rcars/

Link to datasets:http://www.adrl.ethz.ch/software/rcars/datasets

Publication: Michael Neunert, Michael Blösch, Jonas Buchli (2015). An Open Source, Fiducial Based, Visual-Inertial State Estimation System. arXiv, 1507.02081

ROCK* Optimization Algorithm for Policy Learning

The ROCK* algorithm is a sampling-based nonlinear function optimizer which works with many classes of functions. The user should specify the initial search distribution (i.e. the mean and the covariance) then the algorithm finds a minimum of the function. We have shown the performance of ROCK* in very high dimensional systems (500 parameters) as well as low dimensional systems [1]. It outperforms the state-of-the-art algorithm, CMA-ES, sometimes by an order of magnitude. It is also very simple to implement it to different systems and objective functions due to its black-box modeling of the system.

Link to the source code: ROCK* Example Implementation

[1] Jemin Hwangbo, Christian Gehring, Hannes Sommer, Roland Siegwart, Jonas Buchli (2014). ROCK⋆ - Efficient Black-box Optimization for Policy Learning. In Proceedings 2014 IEEE-RAS International Conference on Humanoid Robots PDF

adrl/software.txt · Last modified: 2017/11/03 10:37 by winklera