Publikation
Context-Aware Deep Lagrangian Networks for Model Predictive Control
Lucas Schulze; Jan Peters; Oleg Arenz
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2506.15249, Pages 1-8, arXiv, 2025.
Zusammenfassung
Controlling a robot based on physics-consistent
dynamic models, such as Deep Lagrangian Networks (DeLaN),
can improve the generalizability and interpretability of the
resulting behavior. However, in complex environments, the
number of objects to potentially interact with is vast, and
their physical properties are often uncertain. This complexity
makes it infeasible to employ a single global model. Therefore,
we need to resort to online system identification of context-
aware models that capture only the currently relevant aspects
of the environment. While physical principles such as the
conservation of energy may not hold across varying contexts,
ensuring physical plausibility for any individual context-aware
model can still be highly desirable, particularly when using it
for receding horizon control methods such as model predictive
control (MPC). Hence, in this work, we extend DeLaN to make
it context-aware, combine it with a recurrent network for online
system identification, and integrate it with an MPC for adaptive,
physics-consistent control. We also combine DeLaN with a
residual dynamics model to leverage the fact that a nominal
model of the robot is typically available. We evaluate our
method on a 7-DOF robot arm for trajectory tracking under
varying loads. Our method reduces the end-effector tracking
error by 39%, compared to a 21% improvement achieved by
a baseline that uses an extended Kalman filter.