From Pixels to Torques with Linear Feedback
<|reference_start|>From Pixels to Torques with Linear Feedback: We demonstrate the effectiveness of simple observer-based linear feedback policies for "pixels-to-torques" control of robotic systems using only a robot-facing camera. Specifically, we show that the matrices of an image-based Luenberger observer (linear state estimator) for a "student" output-feedback policy can be learned from demonstration data provided by a "teacher" state-feedback policy via simple linear-least-squares regression. The resulting linear output-feedback controller maps directly from high-dimensional raw images to torques while being amenable to the rich set of analytical tools from linear systems theory, allowing us to enforce closed-loop stability constraints in the learning problem. We also investigate a nonlinear extension of the method via the Koopman embedding. Finally, we demonstrate the surprising effectiveness of linear pixels-to-torques policies on a cartpole system, both in simulation and on real-world hardware. The policy successfully executes both stabilizing and swing-up trajectory tracking tasks using only camera feedback while subject to model mismatch, process and sensor noise, perturbations, and occlusions.<|reference_end|>
@article{lee2024from,
    title={From Pixels to Torques with Linear Feedback},
    author={Jeong Hun Lee, Sam Schoedel, Aditya Bhardwaj, Zachary Manchester},
    journal={arXiv preprint arXiv:2406.18699},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18699},
    primaryClass={cs.RO}
}