Merge pull request #114 from StanfordASL/daniele/update_bib

update CelestiniGammelliEtAl2024

_bibliography/ASL_Bib.bib

@@ -4839,7 +4839,8 @@ @article{CelestiniGammelliEtAl2024
   abstract     = {Model predictive control (MPC) has established itself as the primary methodology for constrained control, enabling general-purpose robot autonomy in diverse real-world scenarios. However, for most problems of interest, MPC relies on the recursive solution of highly non-convex trajectory optimization problems, leading to high computational complexity and strong dependency on initialization. In this work, we present a unified framework to combine the main strengths of optimization-based and learning-based methods for MPC. Our approach entails embedding high-capacity, transformer-based neural network models within the optimization process for trajectory generation, whereby the transformer provides a near-optimal initial guess, or target plan, to a non-convex optimization problem. Our experiments, performed in simulation and the real world onboard a free flyer platform, demonstrate the capabilities of our framework to improve MPC convergence and runtime. Compared to purely optimization-based approaches, results show that our approach can improve trajectory generation performance by up to 75%, reduce the number of solver iterations by up to 45%, and improve overall MPC runtime by 7x without loss in performance.},
   keywords     = {pub},
   owner        = {gammelli},
-  timestamp    = {2024-08-14}
+  timestamp    = {2024-08-14},
+  url          = {https://ieeexplore.ieee.org/document/10685132}
 }
 
 @inproceedings{CauligiCulbertsonEtAl2020,