Update CITATIONS.bib and CITATION.cff

.github/workflows/macos-linux-pixi.yml

@@ -67,7 +67,6 @@ jobs:
         pixi run -e ${{ matrix.environment }} test
 
   check:
-    if: always()
     name: check-macos-linux-pixi
 
     needs:

CITATION.cff

@@ -10,7 +10,12 @@ authors:
     family-names: Jallet
     email: [email protected]
     orcid: 'https://orcid.org/0000-0001-8222-2739'
-    affiliation: LAAS-CNRS & INRIA
+    affiliation: INRIA
+  - given-names: Ewen
+    family-names: Dantec
+    email: [email protected]
+    affiliation: INRIA
+    orcid: 'https://orcid.org/0000-0001-7059-894X'
   - given-names: Antoine
     family-names: Bambade
     affiliation: INRIA

CITATIONS.bib

@@ -19,10 +19,46 @@ @inproceedings{jalletImplicitDifferentialDynamic2022
   abstract = {Over the past decade, the Differential Dynamic Programming (DDP) method has gained in maturity and popularity within the robotics community. Several recent contributions have led to the integration of constraints within the original DDP formulation, hence enlarging its domain of application while making it a strong and easy-to-implement competitor against alternative methods of the state of the art such as collocation or multiple-shooting approaches. Yet, and similarly to its competitors, DDP remains unable to cope with high-dimensional dynamics within a receding horizon fashion, such as in the case of online generation of athletic motion on humanoid robots. In this paper, we propose to make a step toward this objective by reformulating classic DDP as an implicit optimal control problem, allowing the use of more advanced integration schemes such as implicit or variational integrators. To that end, we introduce a primal-dual proximal Lagrangian approach capable of handling dynamic and path constraints in a unified manner, while taking advantage of the time sparsity inherent to optimal control problems. We show that his reformulation enables us to relax the dynamics along the optimization process by solving it inexactly: far from the optimality conditions, the dynamics are only partially fulfilled, but continuously enforced as the solver get closer to the local optimal solution. This inexactness enables our approach to robustly handle large time steps (100 ms or more), unlike other DDP solvers of the state of the art, as experimentally validated through different robotic scenarios.}
 }
 
-@misc{jalletPROXDDPProximalConstrained2023,
+@inproceedings{jalletParallelProximalConstrained2024,
+  title = {Parallel and Proximal Constrained Linear-Quadratic Methods for Real-Time Nonlinear MPC},
+  booktitle = {Proceedings of Robotics: Science and Systems},
+  author = {Jallet, Wilson and Dantec, Ewen and Arlaud, Etienne and Mansard, Nicolas and Carpentier, Justin},
+  year = {2024},
+  month = jul,
+  address = {Delft, Netherlands},
+  doi = {10.15607/RSS.2024.XX.002},
+  abstract = {Recent strides in nonlinear model predictive control (NMPC) underscore a dependence on numerical advancements to efficiently and accurately solve large-scale problems. Given the substantial number of variables characterizing typical whole-body optimal control (OC) problems —often numbering in the thousands— exploiting the sparse structure of the numerical problem becomes crucial to meet computational demands, typically in the range of a few milliseconds. Addressing the linear-quadratic regulator (LQR) problem is a fundamental building block for computing Newton or Sequential Quadratic Programming (SQP) steps in direct optimal control methods. This paper concentrates on equality-constrained problems featuring implicit system dynamics and dual regularization, a characteristic of advanced interiorpoint or augmented Lagrangian solvers. Here, we introduce a parallel algorithm for solving an LQR problem with dual regularization. Leveraging a rewriting of the LQR recursion through block elimination, we first enhanced the efficiency of the serial algorithm and then subsequently generalized it to handle parametric problems. This extension enables us to split decision variables and solve multiple subproblems concurrently. Our algorithm is implemented in our nonlinear numerical optimal control library ALIGATOR. It showcases improved performance over previous serial formulations and we validate its efficacy by deploying it in the model predictive control of a real quadruped robot.},
+  langid = {english}
+}
+
+@inproceedings{dantecCentroidalWholebodyModels2024,
+  title = {From Centroidal to Whole-Body Models for Legged Locomotion: A Comparative Analysis},
+  shorttitle = {From Centroidal to Whole-Body Models for Legged Locomotion},
+  booktitle = {2024 IEEE-RAS 23rd International Conference on Humanoid Robots (Humanoids)},
+  author = {Dantec, Ewen and Jallet, Wilson and Carpentier, Justin},
+  year = {2024},
+  month = nov,
+  pages = {335--342},
+  address = {Nancy, France},
+  issn = {2164-0580},
+  doi = {10.1109/Humanoids58906.2024.10769597},
+  urldate = {2025-04-22},
+  abstract = {Model predictive control is one of the most common methods for stabilizing the dynamics of a legged robot. Yet, it remains unclear which level of complexity should be considered for modeling the system dynamics. On the one hand, most embedded pipelines for legged locomotion rely on reduced models with low computational load in order to ensure real-time capabilities at the price of not exploiting the full potential of the whole-body dynamics. On the other hand, recent numerical solvers can now generate whole-body trajectories on the fly while still respecting tight time constraints. This paper compares the performances of common dynamic models of increasing complexity (centroidal, kino-dynamics, and whole-body models) in simulation over locomotion problems involving challenging gaits, stairs climbing and balance recovery. We also present a 3-D kino-dynamics model that reformulates centroidal dynamics in the coordinates of the base frame by efficiently leveraging the centroidal momentum equation at the acceleration level. This comparative study uses the humanoid robot Talos and the augmented Lagrangian-based solver Aligator to enforce hard constraints on the optimization problem.},
+  keywords = {Analytical models,Complexity theory,Computational modeling,Humanoid robots,Legged locomotion,Load modeling,Mathematical models,Numerical models,Optimization,Robot kinematics}
+}
+
+@article{jalletPROXDDPProximalConstrained2025,
   title = {PROXDDP: Proximal Constrained Trajectory Optimization},
+  shorttitle = {PROXDDP},
   author = {Jallet, Wilson and Bambade, Antoine and Arlaud, Etienne and {El-Kazdadi}, Sarah and Mansard, Nicolas and Carpentier, Justin},
-  year = {2023},
-  abstract = {Trajectory optimization (TO) has proven, over the last decade, to be a versatile and effective framework for robot control. Several numerical solvers have been demonstrated to be fast enough to allow recomputing full-dynamics trajectories for various systems at control time, enabling model predictive control (MPC) of complex robots. These first implementations of MPC in robotics predominantly utilize some differential dynamic programming (DDP) variant for its computational speed and ease of use in constraint-free settings. Nevertheless, many scenarios in robotics call for adding hard constraints in TO problems (e.g., torque limits, obstacle avoidance), which existing solvers, based on DDP, often struggle to handle. Effectively addressing path constraints still poses optimization challenges (e.g., numerical stability, efficiency, accuracy of constraint satisfaction) that we propose to solve by combining advances in numerical optimization with the foundational efficiency of DDP. In this article, we leverage proximal methods for constrained optimization and introduce a DDP-like method to achieve fast, constrained trajectory optimization with an efficient warm-starting strategy particularly suited for MPC applications. Compared to earlier solvers, our approach effectively manages hard constraints without warm-start limitations and exhibits commendable convergence accuracy. Additionally, we leverage the computational efficiency of DDP, enabling real-time resolution of complex problems such as whole-body quadruped locomotion. We provide a complete implementation as part of an open-source and flexible C++ trajectory optimization library called ALIGATOR. These algorithmic contributions are validated through several trajectory planning scenarios from the robotics literature and the real-time whole-body MPC of a quadruped robot.},
-  langid = {english}
+  year = {2025},
+  month = mar,
+  journal = {IEEE Transactions on Robotics},
+  volume = {41},
+  pages = {2605--2624},
+  issn = {1941-0468},
+  doi = {10.1109/TRO.2025.3554437},
+  urldate = {2025-04-04},
+  abstract = {Trajectory optimization has been a popular choice for motion generation and control in robotics for at least a decade. Several numerical approaches have exhibited the required speed to enable online computation of trajectories for real-time of various systems, including complex robots. Many of these said are based on the differential dynamic programming (DDP) algorithm – initially designed for unconstrained trajectory optimization problems – and its variants, which are relatively easy to implement and provide good runtime performance. However, several problems in robot control call for using constrained formulations (e.g. torque limits, obstacle avoidance), from which several difficulties arise when trying to adapt DDP-type methods: numerical stability, computational efficiency, and constraint satisfaction. In this article, we leverage proximal methods for constrained optimization and introduce a DDP-type method for fast, constrained trajectory optimization suited for model-predictive control (MPC) applications with easy warm-starting. Compared to earlier solvers, our approach effectively manages hard constraints without warm-start limitations and exhibits good convergence behavior. We provide a complete implementation as part of an open-source and flexible C++ trajectory optimization library called ALIGATOR. These algorithmic contributions are validated through several trajectory planning scenarios from the robotics literature and the real-time whole-body MPC of a quadruped robot.},
+  keywords = {Convergence,Heuristic algorithms,Legged Robots,Libraries,Linear systems,Minimization,Model-Predictive Control,Newton method,Optimization,Optimization and Optimal Control,Predictive control,Robots,Trajectory optimization}
 }