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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://github.com/ethz-adrl/control-toolbox . The documentation can be accessed at https://ethz-adrl.github.io/ct/
TOWR - Trajectory Optimizer for Walking Robots
A light-weight, Eigen-based C++ library for trajectory optimization for legged robots
Github: https://github.com/ethz-adrl/towr
IFOPT - Eigen-based interface to Nonlinear Programming Solvers
A modern, light-weight, Eigen-based C++ interface to Nonlinear Programming solvers, such as Ipopt and Snopt.
Github: https://github.com/ethz-adrl/ifopt
XPP - Visualization of Legged Robot Motions in RVIZ
 | Xpp is a collection of ROS-packages 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. |
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
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
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