Merge pull request #112 from StanfordASL/update_bib

Update bib

_bibliography/ASL_Bib.bib

@@ -4229,7 +4229,8 @@ @InProceedings{GammelliHarrisonEtAl2023
   month     = jul,
   abstract  = {Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algorithms often requires significant manual trial-and-error. In this work, we argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. To do so, we present network control problems through the lens of reinforcement learning and propose a graph network-based framework to handle a broad class of problems. Instead of naively computing actions over high-dimensional graph elements, e.g., edges, we propose a bi-level formulation where we (1) specify a desired next state via RL, and (2) solve a convex program to best achieve it, leading to drastically improved scalability and performance. We further highlight a collection of desirable features to system designers, investigate design decisions, and present experiments on real-world control problems showing the utility, scalability, and flexibility of our framework.},
   owner     = {jthluke},
-  timestamp = {2024-09-19},
+  timestamp = {2024-09-20},
+  url       = {https://arxiv.org/abs/2305.09129},
 }
 
 @inproceedings{GammelliHarrisonEtAl2022,

_bibliography/ASL_Bib.bib.bak

@@ -4230,6 +4230,7 @@
   abstract  = {Optimization problems over dynamic networks have been extensively studied and widely used in the past decades to formulate numerous real-world problems. However, (1) traditional optimization-based approaches do not scale to large networks, and (2) the design of good heuristics or approximation algorithms often requires significant manual trial-and-error. In this work, we argue that data-driven strategies can automate this process and learn efficient algorithms without compromising optimality. To do so, we present network control problems through the lens of reinforcement learning and propose a graph network-based framework to handle a broad class of problems. Instead of naively computing actions over high-dimensional graph elements, e.g., edges, we propose a bi-level formulation where we (1) specify a desired next state via RL, and (2) solve a convex program to best achieve it, leading to drastically improved scalability and performance. We further highlight a collection of desirable features to system designers, investigate design decisions, and present experiments on real-world control problems showing the utility, scalability, and flexibility of our framework.},
   owner     = {jthluke},
   timestamp = {2024-09-19},
+  url       = {https://arxiv.org/abs/2305.09129},
 }
 
 @inproceedings{GammelliHarrisonEtAl2022,
@@ -5071,7 +5072,7 @@
   timestamp = {2024-09-19}
 }
 
-@Article{BourdillonEtAl2023,
+@Article{BourdillonEtAl2022,
   author    = {Bourdillon, A. and Garg, A. and Wang, H. and Woo, Y. and Pavone, M. and Boyd, J.},
   title     = {Integration of Reinforcement Learning in a Virtual Robotic Surgical Simulation},
   journal   = jrn_SAGE_SI,
@@ -5180,7 +5181,7 @@ Conclusion. Game engines hold promising potential for the design and implementat
   url       = {https://arxiv.org/abs/2403.04057}
 }
 
-@InProceedings{BanerjeeSharmaEtAl2023,
+@InProceedings{BanerjeeSharmaEtAl2022,
   author    = {Banerjee, S. and Sharma, A. and Schmerling, E. and Spolaor, M. and Nemerouf, M. and Pavone, M.},
   title     = {Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications},
   booktitle = proc_IEEE_AC,

_bibliography/AVG_papers.bib

@@ -1355,6 +1355,7 @@ @InProceedings{AntonanteVeerEtAl2023
   year      = {2023},
   address   = {Daegu, Republic of Korea},
   month     = jul,
+  abstract  = {Safety and performance are key enablers for autonomous driving: on the one hand we want our autonomous vehicles (AVs) to be safe, while at the same time their performance (e.g., comfort or progression) is key to adoption. To effectively walk the tightrope between safety and performance, AVs need to be risk-averse, but not entirely risk-avoidant. To facilitate safe-yet-performant driving, in this paper, we develop a task-aware risk estimator that assesses the risk a perception failure poses to the AV’s motion plan. If the failure has no bearing on the safety of the AV’s motion plan, then regardless of how egregious the perception failure is, our task-aware risk estimator considers the failure to have a low risk; on the other hand, if a seemingly benign perception failure severely impacts the motion plan, then our estimator considers it to have a high risk. In this paper, we propose a task-aware risk estimator to decide whether a safety maneuver needs to be triggered. To estimate the task-aware risk, first, we leverage the perception failure — detected by a perception monitor— to synthesize an alternative plausible model for the vehicle’s surroundings. The risk due to the perception failure is then formalized as the “relative” risk to the AV’s motion plan between the perceived and the alternative plausible scenario. We employ a statistical tool called copula, which models tail dependencies between distributions, to estimate this risk. The theoretical properties of the copula allow us to compute probably approximately correct (PAC) estimates of the risk. We evaluate our task-aware risk estimator using NuPlan and compare it with established baselines, showing that the proposed risk estimator achieves the best F1-score (doubling the score of the best baseline) and exhibits a good balance between recall and precision, i.e., a good balance of safety and performance.},
   doi       = {10.15607/RSS.2023.XIX.100},
   owner     = {jthluke},
   timestamp = {2024-09-19},

_bibliography/AVG_papers.bib.bak

@@ -726,11 +726,18 @@
   url       = {https://arxiv.org/abs/2303.07418},
 }
 
-@inproceedings{YangPavone2023b,
+@InProceedings{YangPavone2023b,
   author    = {Yang, H. and Pavone, M.},
-  booktitle = proc_IEEE_CVPR,
   title     = {Object Pose Estimation with Statistical Guarantees: Conformal Keypoint Detection and Geometric Uncertainty Propagation},
+  booktitle = proc_IEEE_CVPR,
   year      = {2023},
+  address   = {Vancouver, Canada},
+  month     = jun,
+  abstract  = {The two-stage object pose estimation paradigm first detects semantic keypoints on the image and then estimates the 6D pose by minimizing reprojection errors. Despite performing well on standard benchmarks, existing techniques offer no provable guarantees on the quality and uncertainty of the estimation. In this paper, we inject two fundamental changes, namely conformal keypoint detection and geometric uncertainty propagation, into the two-stage paradigm and propose the first pose estimator that endows an estimation with provable and computable worst-case error bounds. On one hand, conformal keypoint detection applies the statistical machinery of inductive conformal prediction to convert heuristic keypoint detections into circular or elliptical prediction sets that cover the groundtruth keypoints with a user-specified marginal probability (e.g., 90%). Geometric uncertainty propagation, on the other, propagates the geometric constraints on the keypoints to the 6D object pose, leading to a Pose UnceRtainty SEt (PURSE) that guarantees coverage of the groundtruth pose with the same probability. The PURSE, however, is a nonconvex set that does not directly lead to estimated poses and uncertainties. Therefore, we develop RANdom SAmple averaGing (RANSAG) to compute an average pose and apply semidefinite relaxation to upper bound the worst-case errors between the average pose and the groundtruth. On the LineMOD Occlusion dataset we demonstrate: (i) the PURSE covers the groundtruth with valid probabilities; (ii) the worst-case error bounds provide correct uncertainty quantification; and (iii) the average pose achieves better or similar accuracy as representative methods based on sparse keypoints.},
+  doi       = {10.1109/CVPR52729.2023.00864},
+  owner     = {jthluke},
+  timestamp = {2024-09-20},
+  url       = {https://arxiv.org/abs/2303.12246},
 }
 
 @inproceedings{SunTremblayEtAl2023,