[General] GPU Accelerated Trajectory Optimization with External Transcription

Abierto
  • ¿Algo no está bien?
#676 6 comentarios 0 reacciones 1 asignado Ver en GitHub

Los mantenedores suelen responder en 1 día

@ocots ya está trabajando en esto.

Desde el 15/11/2025.

Evaluación

Este issue todavía no se ha evaluado.

Descripción

@jbcaillau and @ocots,

I am trying to figure out the best way to implement optimal control problems for trajectory optimization in Julia to be solved with GPUs, making the best use of existing packages.

After chatting with @amontoison, I believe that OptimalControl.jl can be a useful framework to communicate with the appropriate modeling tools (e.g., Examodels) and solvers (e.g., those in MadSuit), besides handling the integrator part.

Unfortunately, I haven’t been able to completely derive the dynamics of my quadruped robot from scratch, which I believe is necessary for OptimalControl.jl.

Taking inspiration from TORA.jl (cc @ferrolho), I thought of using RigidBodyDynamics.jl (RBD) to derive them from a URDF file. In TORA.jl, this approach calls the RBD dynamics inference API compatible with AD to calculate both the Jacobian and Hessian. From my chat with @amontoison, I believe this could be made compatible with OptimalControl.jl by using the NLPModels Manual API: https://jso.dev/tutorials/create-a-manual-model/. The issue here is that this approach would require me to extend and ensure that RigidBodyDynamics.jl can perform dynamics inference on the GPU, as far as I know (AFAIK).

A while back, I also tried to rewrite RigidBodyDynamics.jl to directly create the appropriate constraints in a given JuMP model instead of solving the dynamics (https://github.com/andrewrosemberg/RigidBodyDynamics.jl/pull/1). However, it seems that my naive implementation produces extremely large expressions, causing JuMP to stall when trying to check if they belong to the model. I now believe the rewrite would need to be much more intricate, ensuring that the expressions are created cleverly, avoiding redundancy, and being appropriately compatible with how the GPU kernels should be created for good performance.

Sorry for the long post, but I wanted to see what you and others from the community think. :)

Lenguaje dominante
Julia
Estrellas
135
Forks
11
Merge medio
7 h 51 min
PR fusionados (30 d)
22

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de control-toolbox/OptimalControl.jl

Todos los issues de control-toolbox/OptimalControl.jl

Issues similares

Más issues de Julia

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.