# A Data-Driven Approach for Inverse Optimal Control

arXiv: https://arxiv.org/abs/2304.00100 · HTML: https://mlsanity.com/paper/2304.00100/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2023.

## Built on by (most cited first, 3 of 3)

- [Online Control-Informed Learning](https://mlsanity.com/paper/2410.03924/) (2024)
- [Imitation Learning with Limited Actions via Diffusion Planners and Deep Koopman Controllers](https://mlsanity.com/paper/2410.07584/) (2024)
- [Online Intention Prediction via Control-Informed Learning](https://mlsanity.com/paper/2604.09303/) (2026)

## Abstract (arXiv)

This paper proposes a data-driven, iterative approach for inverse optimal control (IOC), which aims to learn the objective function of a nonlinear optimal control system given its states and inputs. The approach solves the IOC problem in a challenging situation when the system dynamics is unknown. The key idea of the proposed approach comes from the deep Koopman representation of the unknown system, which employs a deep neural network to represent observables for the Koopman operator. By assuming the objective function to be learned is parameterized as a linear combination of features with unknown weights, the proposed approach for IOC is able to achieve a Koopman representation of the unknown dynamics and the unknown weights in objective function together. Simulation is provided to verify the proposed approach.