From 5869f71d0e4847275a6f598aa9567b65a4fdfcb6 Mon Sep 17 00:00:00 2001 From: Justin Luke Date: Wed, 30 Oct 2024 17:09:00 -0700 Subject: [PATCH] Updated entries according to Marco's Google Scholar from September 22, 2024 to October 30, 2024. --- _bibliography/ASL_Bib.bib | 64 +++++++++++++++++--------------- _bibliography/ASL_Bib.bib.bak | 69 +++++++++++++++++++++++++---------- 2 files changed, 85 insertions(+), 48 deletions(-) diff --git a/_bibliography/ASL_Bib.bib b/_bibliography/ASL_Bib.bib index dabd2cff..f8a944fd 100755 --- a/_bibliography/ASL_Bib.bib +++ b/_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}, diff --git a/_bibliography/ASL_Bib.bib.bak b/_bibliography/ASL_Bib.bib.bak index 26f214a8..55fcdb74 100644 --- a/_bibliography/ASL_Bib.bib.bak +++ b/_bibliography/ASL_Bib.bib.bak @@ -1307,6 +1307,19 @@ timestamp = {2022-03-01} } +@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, + 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.}, + keywords = {sub}, + owner = {jthluke}, + timestamp = {2024-10-30}, + url = {https://arxiv.org/abs/2410.05585}, +} + @inproceedings{SushkoTedjaratiEtAl2017, author = {A. Sushko and A. Tedjarati and J. Creus-Costa and S. Maldonado and K. Marshland and M. Pavone}, title = {Low cost, high endurance, altitude-controlled latex balloon for near-space research ({ValBal})}, @@ -1811,9 +1824,10 @@ keywords = {sub}, note = {Submitted}, abstract = {Hierarchical policies enable strong performance in many sequential decision-making problems, such as those with high-dimensional action spaces, those requiring long-horizon planning, and settings with sparse rewards. However, learning hierarchical policies from static offline datasets presents a significant challenge. Crucially, actions taken by higher-level policies may not be directly observable within hierarchical controllers, and the offline dataset might have been generated using a different policy structure, hindering the use of standard offline learning algorithms. In this work, we propose OHIO: a framework for offline reinforcement learning (RL) of hierarchical policies. Our framework leverages knowledge of the policy structure to solve the \textit{inverse problem}, recovering the unobservable high-level actions that likely generated the observed data under our hierarchical policy. This approach constructs a dataset suitable for off-the-shelf offline training. We demonstrate our framework on robotic and network optimization problems and show that it substantially outperforms end-to-end RL methods and improves robustness. We investigate a variety of instantiations of our framework, both in direct deployment of policies trained offline and when online fine-tuning is performed.}, - year = {2024}, + year = {2025}, owner = {gammelli}, - timestamp = {2024-08-14} + timestamp = {2024-08-14}, + url = {https://arxiv.org/abs/2410.07933}, } @incollection{SchmerlingPavone2019, @@ -3078,8 +3092,8 @@ month = nov, abstract = {We propose Text2Motion, a language-based planning framework enabling robots to solve sequential manipulation tasks that require long-horizon reasoning. Given a natural language instruction, our framework constructs both a task- and motion-level plan that is verified to reach inferred symbolic goals. Text2Motion uses feasibility heuristics encoded in Q-functions of a library of skills to guide task planning with Large Language Models. Whereas previous language-based planners only consider the feasibility of individual skills, Text2Motion actively resolves geometric dependencies spanning skill sequences by performing geometric feasibility planning during its search. We evaluate our method on a suite of problems that require long-horizon reasoning, interpretation of abstract goals, and handling of partial affordance perception. Our experiments show that Text2Motion can solve these challenging problems with a success rate of 82\%, while prior state-of-the-art language-based planning methods only achieve 13\%. Text2Motion thus provides promising generalization characteristics to semantically diverse sequential manipulation tasks with geometric dependencies between skills.}, doi = {10.1007/s10514-023-10131-7}, - owner = {agia}, - timestamp = {2024-02-29}, + owner = {jthluke}, + timestamp = {2024-10-28}, url = {https://doi.org/10.1007/s10514-023-10131-7}, } @@ -4433,16 +4447,17 @@ 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, @@ -4852,13 +4867,29 @@ 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.}, + title = {Generalizable Spacecraft Trajectory Generation via Multimodal Learning with Transformers}, + booktitle = proc_IEEE_ACC, + 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.}, + keywords = {sub}, + owner = {gammelli}, + timestamp = {2024-10-29}, + url = {https://arxiv.org/abs/2410.11723}, +} + @inproceedings{CauligiCulbertsonEtAl2020, author = {Cauligi, A. and Culbertson, P. and Stellato, B. and Bertsimas, D. and Schwager, M. and Pavone, M.}, title = {Learning Mixed-Integer Convex Optimization Strategies for Robot Planning and Control}, @@ -5461,8 +5492,8 @@ Conclusion. Game engines hold promising potential for the design and implementat address = {Munich, Germany}, booktitle = proc_CoRL, keywords = {press}, - owner = {agia}, - timestamp = {2024-10-20}, + owner = {jthluke}, + timestamp = {2024-10-28}, url = {https://arxiv.org/abs/2410.04640}, }