Merge pull request #122 from StanfordASL/update_bib

Updated entries according to Marco's Google Scholar from September 22…

_bibliography/ASL_Bib.bib

@@ -1307,16 +1307,16 @@ @inproceedings{ThorpeLewEtAl2022
   timestamp = {2022-03-01}
 }
 
-@inproceedings{TakuboGammelliEtAl2024,
-  author    = {Takubo, Y. and Guffanti, T. and Gammelli, D. and Pavone, M. and D'Amico, D.},
+@InProceedings{TakuboGammelliEtAl2024,
+  author    = {Takubo, Y. and Guffanti, T. and Gammelli, D. and Pavone, M. and {D'Amico}, S.},
   title     = {Towards Robust Spacecraft Trajectory Optimization via Transformers},
   booktitle = proc_IEEE_AC,
-  keywords  = {sub},
+  year      = {2025},
   note      = {Submitted},
   abstract  = {Future multi-spacecraft missions require robust autonomous trajectory optimization capabilities to ensure safe and efficient rendezvous operations. This capability hinges on solving non-convex optimal control problems in real time, although traditional iterative methods such as sequential convex programming impose significant computational challenges. To mitigate this burden, the Autonomous Rendezvous Transformer introduced a generative model trained to provide near-optimal initial guesses. This approach provides convergence to better local optima (e.g., fuel optimality), improves feasibility rates, and results in faster convergence speed of optimization algorithms through warm-starting. This work extends the capabilities of ART to address robust chance-constrained optimal control problems. Specifically, ART is applied to challenging rendezvous scenarios in Low Earth Orbit (LEO), ensuring fault-tolerant behavior under uncertainty. Through extensive experimentation, the proposed warm-starting strategy is shown to consistently produce high-quality reference trajectories, achieving up to 30\% cost improvement and 50\% reduction in infeasible cases compared to conventional methods, demonstrating robust performance across multiple state representations. Additionally, a post hoc evaluation framework is proposed to assess the quality of generated trajectories and mitigate runtime failures, marking an initial step toward the reliable deployment of AI-driven solutions in safety-critical autonomous systems such as spacecraft.},
-  year      = {2025},
-  owner     = {gammelli},
-  timestamp = {2024-10-29},
+  keywords  = {sub},
+  owner     = {jthluke},
+  timestamp = {2024-10-30},
   url       = {https://arxiv.org/abs/2410.05585},
 }
 
@@ -3138,17 +3138,19 @@ @inproceedings{LewJansonEtAl2022
   timestamp = {2022-03-01}
 }
 
-@article{LewBonalliPavone2024,
+@Article{LewBonalliPavone2024,
   author    = {Lew, T. and Bonalli, R. and Pavone, M.},
   title     = {Sample Average Approximation for Stochastic Programming with Equality Constraints},
   journal   = jrn_SIAM_JO,
-  note      = {In press},
   year      = {2024},
+  volume    = {34},
+  number    = {4},
+  pages     = {3506--3533},
   abstract  = {We revisit the sample average approximation (SAA) approach for general non-convex stochastic programming. We show that applying the SAA approach to problems with expected value equality constraints does not necessarily result in asymptotic optimality guarantees as the number of samples increases. To address this issue, we relax the equality constraints. Then, we prove the asymptotic optimality of the modified SAA approach under mild smoothness and boundedness conditions on the equality constraint functions. Our analysis uses random set theory and concentration inequalities to characterize the approximation error from the sampling procedure. We apply our approach to the problem of stochastic optimal control for nonlinear dynamical systems subject to external disturbances modeled by a Wiener process. We verify our approach on a rocket-powered descent problem and show that our computed solutions allow for significant uncertainty reduction.},
-  keywords  = {press},
-  owner     = {lew},
-  timestamp = {2022-06-22},
-  url       = {https://arxiv.org/abs/2206.09963}
+  doi       = {doi.org/10.1137/23M1573227},
+  owner     = {jthluke},
+  timestamp = {2024-10-30},
+  url       = {https://arxiv.org/abs/2206.09963},
 }
 
 @article{LewBonalliPavone2023,
@@ -4447,16 +4449,17 @@ @inproceedings{DyroHarrisonEtAl2021
   url       = {https://arxiv.org/abs/2104.02213}
 }
 
-@inproceedings{DyroFoutterEtAl2024,
-  author       = {Dyro, R. and Foutter, M. and Li, R. and Di Lillo, L. and Schmerling, E. and Zhou, X. and Pavone, M.},
-  title        = {Realistic Extreme Behavior Generation for Improved AV Testing},
-  booktitle    = proc_IEEE_ICRA,
-  year         = {2025},
-  abstract     = {This work introduces a framework to diagnose the strengths and shortcomings of Autonomous Vehicle (AV) collision avoidance technology with synthetic yet realistic potential collision scenarios adapted from real-world, collision-free data. Our framework generates counterfactual collisions with diverse crash properties, e.g., crash angle and velocity, between an adversary and a target vehicle by adding perturbations to the adversary's predicted trajectory from a learned AV behavior model. Our main contribution is to ground these adversarial perturbations in realistic behavior as defined through the lens of data-alignment in the behavior model's parameter space. Then, we cluster these synthetic counterfactuals to identify plausible and representative collision scenarios to form the basis of a test suite for downstream AV system evaluation. We demonstrate our framework using two state-of-the-art behavior prediction models as sources of realistic adversarial perturbations, and show that our scenario clustering evokes interpretable failure modes from a baseline AV policy under evaluation.},
-  url          = {/wp-content/papercite-data/pdf/Dyro.Foutter.Li.ea.ICRA2025.pdf},
-  owner        = {foutter},
-  keywords     = {sub},
-  timestamp    = {2024-09-15}
+@InProceedings{DyroFoutterEtAl2024,
+  author    = {Dyro, R. and Foutter, M. and Li, R. and Di Lillo, L. and Schmerling, E. and Zhou, X. and Pavone, M.},
+  title     = {Realistic Extreme Behavior Generation for Improved {AV} Testing},
+  booktitle = proc_IEEE_ICRA,
+  year      = {2025},
+  note      = {Submitted},
+  abstract  = {This work introduces a framework to diagnose the strengths and shortcomings of Autonomous Vehicle (AV) collision avoidance technology with synthetic yet realistic potential collision scenarios adapted from real-world, collision-free data. Our framework generates counterfactual collisions with diverse crash properties, e.g., crash angle and velocity, between an adversary and a target vehicle by adding perturbations to the adversary's predicted trajectory from a learned AV behavior model. Our main contribution is to ground these adversarial perturbations in realistic behavior as defined through the lens of data-alignment in the behavior model's parameter space. Then, we cluster these synthetic counterfactuals to identify plausible and representative collision scenarios to form the basis of a test suite for downstream AV system evaluation. We demonstrate our framework using two state-of-the-art behavior prediction models as sources of realistic adversarial perturbations, and show that our scenario clustering evokes interpretable failure modes from a baseline AV policy under evaluation.},
+  keywords  = {sub},
+  owner     = {jthluke},
+  timestamp = {2024-10-30},
+  url       = {/wp-content/papercite-data/pdf/Dyro.Foutter.Li.ea.ICRA2025.pdf},
 }
 
 @inproceedings{DiCuevasQuiñonesEtAl2024,
@@ -4866,21 +4869,24 @@ @Article{CelestiniGammelliEtAl2024
   title     = {Transformer-based Model Predictive Control: Trajectory Optimization via Sequence Modeling},
   journal   = jrn_IEEE_RAL,
   year      = {2024},
+  volume    = {9},
+  number    = {11},
+  pages     = {9280--9827},
   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},
+  doi       = {10.1109/LRA.2024.3466069},
+  owner     = {jthluke},
+  timestamp = {2024-10-30},
   url       = {https://ieeexplore.ieee.org/document/10685132},
 }
 
-@inproceedings{CelestiniGammelliEtAl2025,
-  author    = {Celestini, D. and Afsharrad, A. and Gammelli, D. and Guffanti, T. and Zardini, G. and Lall, S. and Capelli, E. and D'Amico, S. and Pavone, M.},
+@InProceedings{CelestiniGammelliEtAl2025,
+  author    = {Celestini, D. and Afsharrad, A. and Gammelli, D. and Guffanti, T. and Zardini, G. and Lall, S. and Capelli, E. and {D'Amico}, S. and Pavone, M.},
   title     = {Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers},
   booktitle = proc_IEEE_ACC,
-  keywords  = {sub},
+  year      = {2025},
   note      = {Submitted},
   abstract  = {Effective trajectory generation is essential for reliable on-board spacecraft autonomy. Among other approaches, learning-based warm-starting represents an appealing paradigm for solving the trajectory generation problem, effectively combining the benefits of optimization- and data-driven methods. Current approaches for learning-based trajectory generation often focus on fixed, single-scenario environments, where key scene characteristics, such as obstacle positions or final-time requirements, remain constant across problem instances. However, practical trajectory generation requires the scenario to be frequently reconfigured, making the single-scenario approach a potentially impractical solution. To address this challenge, we present a novel trajectory generation framework that generalizes across diverse problem configurations, by leveraging high-capacity transformer neural networks capable of learning from multimodal data sources. Specifically, our approach integrates transformer-based neural network models into the trajectory optimization process, encoding both scene-level information (e.g., obstacle locations, initial and goal states) and trajectory-level constraints (e.g., time bounds, fuel consumption targets) via multimodal representations. The transformer network then generates near-optimal initial guesses for non-convex optimization problems, significantly enhancing convergence speed and performance. The framework is validated through extensive simulations and real-world experiments on a free-flyer platform, achieving up to 30\% cost improvement and 80\% reduction in infeasible cases with respect to traditional approaches, and demonstrating robust generalization across diverse scenario variations.},
-  year      = {2025},
+  keywords  = {sub},
   owner     = {gammelli},
   timestamp = {2024-10-29},
   url       = {https://arxiv.org/abs/2410.11723},