Below, there is list of the publications that describe or use different components of Crocoddyl:
[1] C. Mastalli, R. Budhiraja, W. Merkt, G. Saurel, B. Hammoud, M. Naveau, J. Carpentier, L. Righetti, S. Vijayakumar and N. Mansard. Crocoddyl: An Efficient and Versatile Framework for Multi-Contact Optimal Control, IEEE International Conference on Robotics and Automation (ICRA), 2020
[2] C. Mastalli, W. Merkt, J. Marti-Saumell, H. Ferrolho, J. Sola, N. Mansard, S. Vijayakumar. A Direct-Indirect Hybridization Approach to Control-Limited DDP, 2021
[3] R. Budhiraja, J. Carpentier, C. Mastalli and N. Mansard. Differential Dynamic Programming for Multi-Phase Rigid Contact Dynamics, IEEE RAS International Conference on Humanoid Robots (ICHR), 2018
Here, there is a list of other contributions that were possible thanks to Crocoddyl:
i. Optimal control:
[4] R. Budhiraja, J. Carpentier and N. Mansard. Dynamics Consensus between Centroidal and Whole-Body Models for Locomotion of Legged Robots, IEEE International Conference on Automation and Robotics (ICRA), 2019
[5] J. Martin-Saumell, J. Sola, C. Mastalli, and A. Santamaria-Navarro. Squash-Box Feasibility Driven Differential Dynamic Programming, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020
ii. Model predictive control:
[6] E. Dantec, R. Budhiraja, A. Roig, T.G. Lembono, G. Saurel, O. Stasse, P. Fernbach, S. Tonneau, S. Vijayakumar, S. Calinon, M. Taïx, N. Mansard. Whole Body Model Predictive Control with a Memory of Motion: Experiments on a Torque-Controlled Talos, IEEE International Conference on Robotics and Automation (ICRA), 2021
[7] T. Corbères, T. Flayols, P.A. Léziart, R. Budhiraja, N. Mansard Comparison of predictive controllers for locomotion and balance recovery of quadruped robots, IEEE International Conference on Robotics and Automation (ICRA), 2021
[8] S. Kleff, A. Meduri, R. Budhiraja, N. Mansard and L. Righetti. High-Frequency Nonlinear Model Predictive Control of a Manipulator, IEEE International Conference on Robotics and Automation (ICRA), 2021
iii. Machine learning:
[9] T. G. Lembono, C. Mastalli, P. Fernbach, N. Mansard and S. Calinon. Learning How to Walk: Warm-starting Optimal Control Solver with Memory of Motion, IEEE International Conference on Robotics and Automation (ICRA), 2020
iv. Co-design
[10] G Fadini, T Flayols, A del Prete, N Mansard, P Souères. Computational design of energy-efficient legged robots: Optimizing for size and actuators, IEEE International Conference on Robotics and Automation (ICRA), 2021
[11] T. Dinev, C. Mastalli, V. Ivan, S. Tonneau and S. Vijayakumar. Co-Designing Robots by Differentiating Motion Solvers, 2021
v. Biomechanics
[12] M. Boukheddimi, R. Budhiraja, P. Souères and B. Watier. Anthropomorphic Gait Generation using Differential Dynamic Programming with a Reduced Number of Cost Criteria, 2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob), 2020