Pixels to Torques with Linear Feedback

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, alowing 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.

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Related Papers

2024
October
PDF From Pixels to Torques with Linear Feedback
Jeong Hun Lee, Sam Schoedel, Aditya Bhardwaj, and Zachary Manchester
International Workshop on the Algorithmic Foundations of Robotics (WAFR). Chicago, IL. (In Review)

People

Jeong Hun (JJ) Lee
Swimming Dynamics and Control
Sam Schoedel
Optimization-based Planning and Control
Zac Manchester
Assistant Professor
Last updated: 2024-06-26