CusADi: A GPU Parallelization Framework for Symbolic Expressions and Optimal Control

The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into this process remains challenging due to the complexity of formulating and solving optimization problems across thousands of...

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Bibliographic Details
Published in:IEEE robotics and automation letters Vol. 10; no. 2; pp. 899 - 906
Main Authors: Jeon, Se Hwan, Hong, Seungwoo, Lee, Ho Jae, Khazoom, Charles, Kim, Sangbae
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.02.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Closed form solutions
Codes
Controllers
Exact solutions
Graphics processing units
Optimal control
Optimization
Optimization and optimal control
Reinforcement learning
Robot programming
Software tools
software tools for robot programming
Trajectory
ISSN:2377-3766, 2377-3766
Online Access:Get full text
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Summary:The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into this process remains challenging due to the complexity of formulating and solving optimization problems across thousands of instances. In this work, we present CusADi , an extension of the casadi symbolic framework to support the parallelization of arbitrary closed-form expressions on GPUs with CUDA . We also formulate a closed-form approximation for solving general optimal control problems, enabling large-scale parallelization and evaluation of MPC controllers. Our results show a ten-fold speedup relative to similar MPC implementation on the CPU, and we demonstrate the use of CusADi for various applications, including parallel simulation, parameter sweeps, and policy training.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2024.3512254