Overview

OCS2 is a C++ toolbox tailored for Optimal Control for Switched Systems (OCS2). The toolbox provides an efficient implementation of the following algorithms:

• SLQ: Continuous-time domain constrained DDP.

• iLQR: Discrete-time domain constrained DDP.

• SQP: Multiple-shooting algorithm based on HPIPM.

• SLP: Sequential Linear Programming based on PIPG.

• IPM: Multiple-shooting algorithm based on nonlinear interior point method.

OCS2 handles general path constraints through Augmented Lagrangian or relaxed barrier methods. To facilitate the application of OCS2 in robotic tasks, it provides the user with additional tools to set up the system dynamics (such as kinematic or dynamic models) and cost/constraints (such as self-collision avoidance and end-effector tracking) from a URDF model. The library also provides an automatic differentiation tool to calculate derivatives of the system dynamics, constraints, and cost. To facilitate its deployment on robotic platforms, the OCS2 provides tools for ROS interfaces. The toolbox’s efficient and numerically stable implementations in conjunction with its user-friendly interface have paved the way for employing it on numerous robotic applications with limited onboard computation power.

How to use the OCS2 toolbox?

OCS2 can be easily installed on Ubuntu. The source code is also publicly available. To get started with the control toolbox, please refer to the Installation started, and Getting Started pages.

Licence

The OCS2 toolbox is released under the BSD 3-Clause license. Please note the license and notice files in the source directory.

Credits

The following people have been involved in the development of OCS2:

Project Manager: Farbod Farshidian.

Main Developers: Farbod Farshidian, Ruben Grandia, Michael Spieler, Jan Carius, Jean-Pierre Sleiman.

Other Developers: Alexander Reske, Sotaro Katayama, Mayank Mittal, Jia-​Ruei Chiu, Johannes Pankert, Perry Franklin, Tom Lankhorst, David Hoeller, Asutosh Satapathy, Markus Giftthaler, Edo Jelavic.

Acknowledgement: The OCS2 toolbox development initiated by the ADRL team at ETH Zurich, and the project has continued to evolve at RSL, ETH Zurich. The RSL team now actively supports the development of OCS2.

Citing OCS2

To cite the toolbox in your academic research, please use the following:

@misc{OCS2,
   title = {{OCS2}:  An  open  source  library  for  Optimal  Control  of  Switched Systems},
   note = {[Online]. Available: \url{https://github.com/leggedrobotics/ocs2}},
   author = {Farbod Farshidian and others}
}

Tutorials

• Tutorial on OCS2 toolbox, Farbod Farshidian, MPC Workshop, RSS 2021 (link).

• Real-time optimal control for legged locomotion and manipulation, Marco Hutter, MPC Workshop, RSS 2021 (link).

Related publications

This toolbox has been used in the following publications:

1. Farbod Farshidian, Michael Neunert, Alexander W Winkler, Gonzalo Rey, and Jonas Buchli. An efficient optimal planning and control framework for quadrupedal locomotion. In 2017 IEEE International Conference on Robotics and Automation (ICRA), 93–100. IEEE, 2017.

2. Farbod Farshidian, Maryam Kamgarpour, Diego Pardo, and Jonas Buchli. Sequential linear quadratic optimal control for nonlinear switched systems. IFAC, 50(1):1463–1469, 2017.

3. Farbod Farshidian, Edo Jelavic, Asutosh Satapathy, Markus Giftthaler, and Jonas Buchli. Real-time motion planning of legged robots: a model predictive control approach. In 2017 IEEE-RAS 17th International Conference on Humanoid Robotics (Humanoids), 577–584. IEEE, 2017.

4. Markus Giftthaler, Farbod Farshidian, Timothy Sandy, Lukas Stadelmann, and Jonas Buchli. Efficient kinematic planning for mobile manipulators with non-holonomic constraints using optimal control. In 2017 IEEE International Conference on Robotics and Automation (ICRA), 3411–3417. IEEE, 2017.

5. Farbod Farshidian, David Hoeller, and Marco Hutter. Deep value model predictive control. In Conference on Robot Learning (CoRL), 990–1004. PMLR, 2020.

6. Ruben Grandia, Farbod Farshidian, Alexey Dosovitskiy, René Ranftl, and Marco Hutter. Frequency-aware model predictive control. IEEE Robotics and Automation Letters, 4(2):1517–1524, 2019.

7. Maria Vittoria Minniti, Farbod Farshidian, Ruben Grandia, and Marco Hutter. Whole-body mpc for a dynamically stable mobile manipulator. IEEE Robotics and Automation Letters, 4(4):3687–3694, 2019.

8. Jan Carius, Farbod Farshidian, and Marco Hutter. Mpc-net: a first principles guided policy search. IEEE Robotics and Automation Letters, 5(2):2897–2904, 2020.

9. Ruben Grandia, Farbod Farshidian, René Ranftl, and Marco Hutter. Feedback mpc for torque-controlled legged robots. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 4730–4737. IEEE, 2019.

10. Abel Gawel, Hermann Blum, Johannes Pankert, Koen Krämer, Luca Bartolomei, Selen Ercan, Farbod Farshidian, Margarita Chli, Fabio Gramazio, Roland Siegwart, and others. A fully-integrated sensing and control system for high-accuracy mobile robotic building construction. In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2300–2307. IEEE, 2019.

11. Jean-Pierre Sleiman, Farbod Farshidian, Maria Vittoria Minniti, and Marco Hutter. A unified mpc framework for whole-body dynamic locomotion and manipulation. IEEE Robotics and Automation Letters, 6(3):4688–4695, 2021.

12. Ruben Grandia, Andrew J Taylor, Aaron D Ames, and Marco Hutter. Multi-layered safety for legged robots via control barrier functions and model predictive control. arXiv preprint arXiv:2011.00032, 2020.

13. Magnus Gaertner, Marko Bjelonic, Farbod Farshidian, and Marco Hutter. Collision-free mpc for legged robots in static and dynamic scenes. In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021.

14. Jean-Pierre Sleiman, Farbod Farshidian, and Marco Hutter. Constraint handling in continuous-time ddp-based model predictive control. In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021.

15. Alexander Reske, Jan Carius, Yuntao Ma, Farbod Farshidian, and Marco Hutter. Imitation learning from mpc for quadrupedal multi-gait control. In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021.

16. Maria Vittoria Minniti, Ruben Grandia, Kevin Fäh, Farbod Farshidian, and Marco Hutter. Model predictive robot-environment interaction control for mobile manipulation tasks. In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021.

17. Mayank Mittal, David Hoeller, Farbod Farshidian, Marco Hutter, and Animesh Garg. Articulated object interaction in unknown scenes with whole-body mobile manipulation. arXiv preprint arXiv:2103.10534, 2021.