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Equality Constrained Differential Dynamic Programming

Sarah Kazdadi 1 Justin Carpentier 1 Jean Ponce 1
1 WILLOW - Models of visual object recognition and scene understanding
DI-ENS - Département d'informatique de l'École normale supérieure, Inria de Paris
Abstract : Trajectory optimization is an important tool in task-based robot motion planning, due to its generality and convergence guarantees under some mild conditions. It is often used as a post-processing operation to smooth out trajectories that are generated by probabilistic methods or to directly control the robot motion. Unconstrained trajectory optimization problems have been well studied, and are commonly solved using Differential Dynamic Programming methods that allow for fast convergence at a relatively low computational cost. In this paper, we propose an augmented Lagrangian approach that extends these ideas to equality-constrained trajectory optimization problems, while maintaining a balance between convergence speed and numerical stability. We illustrate our contributions on various standard robotic problems and highlights their benefits compared to standard approaches.
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https://hal.inria.fr/hal-03184203
Contributor : Justin Carpentier <>
Submitted on : Wednesday, April 7, 2021 - 5:03:53 PM
Last modification on : Friday, April 9, 2021 - 3:32:19 AM

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Sarah Kazdadi, Justin Carpentier, Jean Ponce. Equality Constrained Differential Dynamic Programming. ICRA 2021 - IEEE International Conference on Robotics and Automation, May 2021, Xi’an, China. ⟨hal-03184203v2⟩

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