Marco Pavone
Associate Professor of Aeronautics and Astronautics and, by courtesy, of Electrical Engineering and of Computer Science
Bio
Dr. Marco Pavone is an Associate Professor of Aeronautics and Astronautics at Stanford University, where he directs the Autonomous Systems Laboratory and the Center for Automotive Research at Stanford. He is also a Distinguished Research Scientist at NVIDIA where he leads autonomous vehicle research. Before joining Stanford, he was a Research Technologist within the Robotics Section at the NASA Jet Propulsion Laboratory. He received a Ph.D. degree in Aeronautics and Astronautics from the Massachusetts Institute of Technology in 2010. His main research interests are in the development of methodologies for the analysis, design, and control of autonomous systems, with an emphasis on self-driving cars, autonomous aerospace vehicles, and future mobility systems. He is a recipient of a number of awards, including a Presidential Early Career Award for Scientists and Engineers from President Barack Obama, an Office of Naval Research Young Investigator Award, a National Science Foundation Early Career (CAREER) Award, a NASA Early Career Faculty Award, and an Early-Career Spotlight Award from the Robotics Science and Systems Foundation. He was identified by the American Society for Engineering Education (ASEE) as one of America's 20 most highly promising investigators under the age of 40. His work has been recognized with best paper nominations or awards at a number of venues, including the European Conference on Computer Vision, the IEEE International Conference on Robotics and Automation, the European Control Conference, the IEEE International Conference on Intelligent Transportation Systems, the Field and Service Robotics Conference, the Robotics: Science and Systems Conference, and the INFORMS Annual Meeting.
Academic Appointments
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Associate Professor, Aeronautics and Astronautics
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Associate Professor (By courtesy), Electrical Engineering
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Associate Professor (By courtesy), Computer Science
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Faculty Affiliate, Institute for Human-Centered Artificial Intelligence (HAI)
Honors & Awards
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PECASE Award, White House (2017)
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YIP Award, ONR (2017)
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CAREER Award, NSF (2015)
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Frontiers of Engineering Program, National Academy of Engineering (2013)
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Early Career Faculty award, NASA (2012)
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Hellman Faculty Scholar Award, Hellman Fellows Fund (2012)
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NIAC Fellow, NASA (2011)
Professional Education
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Ph.D., MIT, Aeronautics and Astronautics (2010)
2024-25 Courses
- Optimal and Learning-based Control
AA 203 (Spr) - Principles of Robot Autonomy I
AA 174A, CS 137A (Aut) - Principles of Robot Autonomy II
AA 174B, AA 274B, CS 237B, EE 260B, ME 274B (Win) - Robotics and Autonomous Systems Seminar
ENGR 319 (Aut, Win, Spr) -
Independent Studies (20)
- Advanced Reading and Research
CS 499 (Aut, Win, Spr, Sum) - Advanced Reading and Research
CS 499P (Aut, Win, Spr, Sum) - Curricular Practical Training
CME 390 (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390A (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390B (Aut, Win, Spr, Sum) - Curricular Practical Training
CS 390C (Aut, Win, Spr, Sum) - Directed Research and Writing in Aero/Astro
AA 190 (Aut, Win, Spr, Sum) - Experimental Investigation of Engineering Problems
ME 392 (Aut, Win, Spr, Sum) - Independent Project
CS 399 (Aut, Win, Spr, Sum) - Independent Project
CS 399P (Aut, Win, Spr, Sum) - Independent Study in Aero/Astro
AA 199 (Aut, Win, Spr, Sum) - Independent Work
CS 199 (Aut, Win, Spr, Sum) - Independent Work
CS 199P (Aut, Win, Spr, Sum) - Master's Research
CME 291 (Aut, Win, Spr, Sum) - Part-time Curricular Practical Training
CS 390D (Aut, Win, Spr, Sum) - Ph.D. Research
CME 400 (Aut, Win, Spr, Sum) - Ph.D. Research Rotation
CME 391 (Aut, Sum) - Practical Training
AA 291 (Aut, Win, Spr, Sum) - Problems in Aero/Astro
AA 290 (Aut, Win, Spr) - Supervised Undergraduate Research
CS 195 (Aut, Win, Spr, Sum)
- Advanced Reading and Research
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Prior Year Courses
2023-24 Courses
- Optimal and Learning-based Control
AA 203 (Spr) - Principles of Robot Autonomy I
AA 174A, CS 137A, EE 160A (Aut) - Principles of Robot Autonomy II
AA 174B, AA 274B, CS 237B (Win) - Robotics and Autonomous Systems Seminar
AA 289, CS 529 (Aut, Win, Spr)
2022-23 Courses
- Principles of Robot Autonomy II
AA 174B, AA 274B, CS 237B, EE 260B (Win) - Robotics and Autonomous Systems Seminar
AA 289, CS 529 (Aut, Win, Spr)
2021-22 Courses
- Principles of Robot Autonomy II
AA 174B, AA 274B, CS 237B, EE 260B (Win) - Robotics and Autonomous Systems Seminar
AA 289, CS 529 (Win, Spr)
- Optimal and Learning-based Control
Stanford Advisees
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Doctoral Dissertation Reader (AC)
Fadhil Ginting, Liam Kruse, Justin Luke, Emi Soroka, Alexandros Tzikas, Anil Yildiz -
Postdoctoral Faculty Sponsor
Daniele Gammelli -
Doctoral Dissertation Advisor (AC)
John Alora, Amine Elhafsi, Devansh Jalota, Luis Pabon -
Orals Evaluator
John Alora -
Master's Program Advisor
Igor Barakaiev, Jerry Chan, Anshuk Chigullapalli, Derek Chong, Chenyang Dai, Aviad Golan Peretz, Pierre Labroche, Ian Lim, Emily Okabe, Bora Oztekin, Adrien Richez, Kristopher Riordan, Julian Rodriguez Cardenas, Matthew Simpson, Mike Timmerman, Owen Veit, Nyle Wong, Lucas Yantis, Sonny Young -
Doctoral Dissertation Co-Advisor (AC)
Michelle Ho, Guillem Megias i Homar -
Doctoral (Program)
Pranit Mohnot
All Publications
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Optimal coordination of electric buses and battery storage for achieving a 24/7 carbon-free electrified fleet
Applied Energy
2025; 377
View details for DOI 10.1016/j.apenergy.2024.124506
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RealGen: Retrieval Augmented Generation for Controllable Traffic Scenarios
SPRINGER INTERNATIONAL PUBLISHING AG. 2025: 93-110
View details for DOI 10.1007/978-3-031-73033-7_6
View details for Web of Science ID 001353685900006
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Transformer-Based Model Predictive Control: Trajectory Optimization via Sequence Modeling
IEEE ROBOTICS AND AUTOMATION LETTERS
2024; 9 (11): 9820-9827
View details for DOI 10.1109/LRA.2024.3466069
View details for Web of Science ID 001327438100028
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Gradient Descent-Based Task-Orientation Robot Control Enhanced With Gaussian Process Predictions
IEEE ROBOTICS AND AUTOMATION LETTERS
2024; 9 (9): 8035-8042
View details for DOI 10.1109/LRA.2024.3438039
View details for Web of Science ID 001291902500008
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On the Interplay Between Self-Driving Cars and Public Transportation
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
2024; 11 (3): 1478-1490
View details for DOI 10.1109/TCNS.2023.3338248
View details for Web of Science ID 001322535500019
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Estimating the Convex Hull of the Image of a Set with Smooth Boundary: Error Bounds and Applications
DISCRETE & COMPUTATIONAL GEOMETRY
2024
View details for DOI 10.1007/s00454-024-00683-5
View details for Web of Science ID 001299709500001
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When Efficiency Meets Equity in Congestion Pricing and Revenue Refunding Schemes
IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS
2024; 11 (2): 1127-1138
View details for DOI 10.1109/TCNS.2023.3333413
View details for Web of Science ID 001252775800033
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Locomotion as manipulation with ReachBot.
Science robotics
2024; 9 (89): eadi9762
Abstract
Caves and lava tubes on the Moon and Mars are sites of geological and astrobiological interest but consist of terrain that is inaccessible with traditional robot locomotion. To support the exploration of these sites, we present ReachBot, a robot that uses extendable booms as appendages to manipulate itself with respect to irregular rock surfaces. The booms terminate in grippers equipped with microspines and provide ReachBot with a large workspace, allowing it to achieve force closure in enclosed spaces, such as the walls of a lava tube. To propel ReachBot, we present a contact-before-motion planner for nongaited legged locomotion that uses internal force control, similar to a multifingered hand, to keep its long, slender booms in tension. Motion planning also depends on finding and executing secure grips on rock features. We used a Monte Carlo simulation to inform gripper design and predict grasp strength and variability. In addition, we used a two-step perception system to identify possible grasp locations. To validate our approach and mechanisms under realistic conditions, we deployed a single ReachBot arm and gripper in a lava tube in the Mojave Desert. The field test confirmed that ReachBot will find many targets for secure grasps with the proposed kinematic design.
View details for DOI 10.1126/scirobotics.adi9762
View details for PubMedID 38630805
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Bayesian Embeddings for Few-Shot Open World Recognition.
IEEE transactions on pattern analysis and machine intelligence
2024; 46 (3): 1513-1529
Abstract
As autonomous decision-making agents move from narrow operating environments to unstructured worlds, learning systems must move from a closed-world formulation to an open-world and few-shot setting in which agents continuously learn new classes from small amounts of information. This stands in stark contrast to modern machine learning systems that are typically designed with a known set of classes and a large number of examples for each class. In this work we extend embedding-based few-shot learning algorithms to the open-world recognition setting. We combine Bayesian non-parametric class priors with an embedding-based pre-training scheme to yield a highly flexible framework which we refer to as few-shot learning for open world recognition (FLOWR). We benchmark our framework on open-world extensions of the common MiniImageNet and TieredImageNet few-shot learning datasets. Our results show, compared to prior methods, strong classification accuracy performance and up to a 12% improvement in H-measure (a measure of novel class detection) from our non-parametric open-world few-shot learning scheme.
View details for DOI 10.1109/TPAMI.2022.3201541
View details for PubMedID 36063507
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Interactive Joint Planning for Autonomous Vehicles
IEEE ROBOTICS AND AUTOMATION LETTERS
2024; 9 (2): 987-994
View details for DOI 10.1109/LRA.2023.3332474
View details for Web of Science ID 001129132400010
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Risk-Averse Trajectory Optimization via Sample Average Approximation
IEEE ROBOTICS AND AUTOMATION LETTERS
2024; 9 (2): 1500-1507
View details for DOI 10.1109/LRA.2023.3331889
View details for Web of Science ID 001138708600002
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A COPOSITIVE FRAMEWORK FOR ANALYSIS OF HYBRID ISING-CLASSICAL ALGORITHMS
SIAM JOURNAL ON OPTIMIZATION
2024; 34 (2): 1455-1489
View details for DOI 10.1137/22M1514581
View details for Web of Science ID 001207741200001
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Accelerating Continuous Variable Coherent Ising Machines via Momentum
SPRINGER INTERNATIONAL PUBLISHING AG. 2024: 109-126
View details for DOI 10.1007/978-3-031-60597-0_8
View details for Web of Science ID 001283958800008
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SAMPLE AVERAGE APPROXIMATION FOR STOCHASTIC PROGRAMMING WITH EQUALITY CONSTRAINTS
SIAM JOURNAL ON OPTIMIZATION
2024; 34 (4): 3506-3533
View details for DOI 10.1137/23M1573227
View details for Web of Science ID 001345345200014
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Transformers for Trajectory Optimization with Application to Spacecraft Rendezvous
IEEE. 2024
View details for DOI 10.1109/AERO58975.2024.10521334
View details for Web of Science ID 001229648404007
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Modeling Considerations for Developing Deep Space Autonomous Spacecraft and Simulators
IEEE. 2024
View details for DOI 10.1109/AERO58975.2024.10521055
View details for Web of Science ID 001229648400079
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Contingency Planning Using Bi-level Markov Decision Processes for Space Missions
IEEE. 2024
View details for DOI 10.1109/AERO58975.2024.10521281
View details for Web of Science ID 001229648403043
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Martian Exploration of Lava Tubes (MELT) with ReachBot: Scientific Investigation and Concept of Operations
IEEE. 2024: 36-41
View details for DOI 10.1109/iSpaRo60631.2024.10687389
View details for Web of Science ID 001327765400006
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Partial-View Object View Synthesis via Filtering Inversion
IEEE COMPUTER SOC. 2024: 453-463
View details for DOI 10.1109/3DV62453.2024.00105
View details for Web of Science ID 001250581700033
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Dynamic Locational Marginal Emissions via Implicit Differentiation
IEEE TRANSACTIONS ON POWER SYSTEMS
2024; 39 (1): 1138-1147
View details for DOI 10.1109/TPWRS.2023.3247345
View details for Web of Science ID 001136086900088
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Sample-efficient safety assurances using conformal prediction
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
2023
View details for DOI 10.1177/02783649231221580
View details for Web of Science ID 001126253500001
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Text2Motion: from natural language instructions to feasible plans
AUTONOMOUS ROBOTS
2023; 47 (8): 1345-1365
View details for DOI 10.1007/s10514-023-10131-7
View details for Web of Science ID 001281797200002
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The matroid team surviving orienteers problem and its variants: Constrained routing of heterogeneous teams with risky traversal
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
2023
View details for DOI 10.1177/02783649231210326
View details for Web of Science ID 001090814800001
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Semantic anomaly detection with large language models
AUTONOMOUS ROBOTS
2023
View details for DOI 10.1007/s10514-023-10132-6
View details for Web of Science ID 001097671300001
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Balancing fairness and efficiency in traffic routing via interpolated traffic assignment
AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS
2023; 37 (2)
View details for DOI 10.1007/s10458-023-09616-7
View details for Web of Science ID 001041850100001
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Near-Optimal Multi-Robot Motion Planning with Finite Sampling
IEEE TRANSACTIONS ON ROBOTICS
2023; 39 (5): 3422-3436
View details for DOI 10.1109/TRO.2023.3281152
View details for Web of Science ID 001098371200005
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Analysis of Theoretical and Numerical Properties of Sequential Convex Programming for Continuous-Time Optimal Control
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2023; 68 (8): 4570-4585
View details for DOI 10.1109/TAC.2022.3207865
View details for Web of Science ID 001041305400006
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Robust feedback motion planning via contraction theory
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
2023; 42 (9): 655-688
View details for DOI 10.1177/02783649231186165
View details for Web of Science ID 001058674200002
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Fisher markets with linear constraints: Equilibrium properties and efficient distributed algorithms
GAMES AND ECONOMIC BEHAVIOR
2023; 141: 223-260
View details for DOI 10.1016/j.geb.2023.06.007
View details for Web of Science ID 001039441900001
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Control-oriented meta-learning
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
2023
View details for DOI 10.1177/02783649231165085
View details for Web of Science ID 001006327300001
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Trustworthy AI-Part II
COMPUTER
2023; 56 (5): 13-16
View details for DOI 10.1109/MC.2023.3253980
View details for Web of Science ID 000982607400003
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Real-Time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms
IEEE ROBOTICS AND AUTOMATION LETTERS
2023; 8 (4): 2397-2404
View details for DOI 10.1109/LRA.2023.3246839
View details for Web of Science ID 000952959800014
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Co-design of communication and machine inference for cloud robotics
AUTONOMOUS ROBOTS
2023
View details for DOI 10.1007/s10514-023-10093-w
View details for Web of Science ID 000956214700001
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Co-Design to Enable User-Friendly Tools to Assess the Impact of Future Mobility Solutions
IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING
2023; 10 (2): 827-844
View details for DOI 10.1109/TNSE.2022.3223912
View details for Web of Science ID 000966994400001
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Online Routing Over Parallel Networks: Deterministic Limits and Data-driven Enhancements
INFORMS JOURNAL ON COMPUTING
2023
View details for DOI 10.1287/ijoc.2023.1275
View details for Web of Science ID 000971333200001
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Trustworthy AI-Part 1
COMPUTER
2023; 56 (2): 14-18
View details for DOI 10.1109/MC.2022.3227683
View details for Web of Science ID 000935665700006
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Data-Driven Spectral Submanifold Reduction for Nonlinear Optimal Control of High-Dimensional Robots
IEEE. 2023: 2627-2633
View details for DOI 10.1109/ICRA48891.2023.10160418
View details for Web of Science ID 001036713002004
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trajdata: A Unified Interface to Multiple Human Trajectory Datasets
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023
View details for Web of Science ID 001229826606004
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Hybrid Multi-agent Deep Reinforcement Learning for Autonomous Mobility on Demand Systems
JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023
View details for Web of Science ID 001221742900098
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Multi-Predictor Fusion: Combining Learning-based and Rule-based Trajectory Predictors
JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023
View details for Web of Science ID 001221201502046
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Language-Guided Traffic Simulation via Scene-Level Diffusion
JMLR-JOURNAL MACHINE LEARNING RESEARCH. 2023
View details for Web of Science ID 001221201500007
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Interpretable Trajectory Prediction for Autonomous Vehicles via Counterfactual Responsibility
IEEE. 2023: 5918-5925
View details for DOI 10.1109/IROS55552.2023.10341712
View details for Web of Science ID 001136907800085
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PAC-Bayes Generalization Certificates for Learned Inductive Conformal Prediction
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2023
View details for Web of Science ID 001227224006030
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Practical Deployment of Spectral Submanifold Reduction for Optimal Control of High-Dimensional Systems
ELSEVIER. 2023: 4074-4081
View details for DOI 10.1016/j.ifacol.2023.10.1734
View details for Web of Science ID 001196709200158
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Robust-RRT: Probabilistically-Complete Motion Planning for Uncertain Nonlinear Systems
SPRINGER INTERNATIONAL PUBLISHING AG. 2023: 538-554
View details for DOI 10.1007/978-3-031-25555-7_36
View details for Web of Science ID 001008380600036
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Sample-Efficient Safety Assurances Using Conformal Prediction
SPRINGER INTERNATIONAL PUBLISHING AG. 2023: 149-169
View details for DOI 10.1007/978-3-031-21090-7_10
View details for Web of Science ID 000978413100010
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Designing ReachBot: System Design Process with a Case Study of a Martian Lava Tube Mission
IEEE. 2023
View details for DOI 10.1109/AERO55745.2023.10115893
View details for Web of Science ID 001008282004039
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Data Lifecycle Management in Evolving Input Distributions for Learning-based Aerospace Applications
IEEE. 2023
View details for DOI 10.1109/AERO55745.2023.10115970
View details for Web of Science ID 001008282005029
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Expanding the Deployment Envelope of Behavior Prediction via Adaptive Meta-Learning
IEEE. 2023: 7786-7793
View details for DOI 10.1109/ICRA48891.2023.10161155
View details for Web of Science ID 001048371101018
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Tree-structured Policy Planning with Learned Behavior Models
IEEE. 2023: 7902-7908
View details for DOI 10.1109/ICRA48891.2023.10161419
View details for Web of Science ID 001048371101034
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Learning Responsibility Allocations for Safe Human-Robot Interaction with Applications to Autonomous Driving
IEEE. 2023: 9757-9763
View details for DOI 10.1109/ICRA48891.2023.10161112
View details for Web of Science ID 001048371102064
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Motion Planning for a Climbing Robot with Stochastic Grasps
IEEE. 2023: 11838-11844
View details for DOI 10.1109/ICRA48891.2023.10160218
View details for Web of Science ID 001048371103133
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Learning Autonomous Vehicle Safety Concepts from Demonstrations
IEEE. 2023: 3193-3200
View details for Web of Science ID 001027160302130
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FreeNeRF: Improving Few-shot Neural Rendering with Free Frequency Regularization
IEEE COMPUTER SOC. 2023: 8254-8263
View details for DOI 10.1109/CVPR52729.2023.00798
View details for Web of Science ID 001062522100025
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Differentially Private Stochastic Convex Optimization for Network Routing Applications
IEEE. 2023: 7475-7482
View details for Web of Science ID 001166433806029
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Exact Characterization of the Convex Hulls of Reachable Sets
IEEE. 2023: 52-59
View details for DOI 10.1109/CDC49753.2023.10383902
View details for Web of Science ID 001166433800008
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Credit-Based Congestion Pricing: Equilibrium Properties and Optimal Scheme Design
IEEE. 2023: 4124-4129
View details for DOI 10.1109/CDC49753.2023.10384266
View details for Web of Science ID 001166433803066
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Robust Nonlinear Reduced-Order Model Predictive Control
IEEE. 2023: 4798-4805
View details for DOI 10.1109/CDC49753.2023.10383243
View details for Web of Science ID 001166433803151
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Closing the Loop on Runtime Monitors with Fallback-Safe MPC
IEEE. 2023: 6533-6540
View details for DOI 10.1109/CDC49753.2023.10383965
View details for Web of Science ID 001166433805057
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Receding Horizon Planning with Rule Hierarchies for Autonomous Vehicles
IEEE. 2023: 1507-1513
View details for DOI 10.1109/ICRA48891.2023.10160622
View details for Web of Science ID 001036713001047
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BITS: Bi-level Imitation for Traffic Simulation
IEEE. 2023: 2929-2936
View details for DOI 10.1109/ICRA48891.2023.10161167
View details for Web of Science ID 001036713002046
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Planning with Occluded Traffic Agents using Bi-Level Variational Occlusion Models
IEEE. 2023: 5558-5565
View details for DOI 10.1109/ICRA48891.2023.10160604
View details for Web of Science ID 001036713004077
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Guided Conditional Diffusion for Controllable Traffic Simulation
IEEE. 2023: 3560-3566
View details for DOI 10.1109/ICRA48891.2023.10161463
View details for Web of Science ID 001036713002134
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Risk-Sensitive Safety Analysis Using Conditional Value-at-Risk
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2022; 67 (12): 6521-6536
View details for DOI 10.1109/TAC.2021.3131149
View details for Web of Science ID 000895440500014
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Linear Reduced-Order Model Predictive Control
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2022; 67 (11): 5980-5995
View details for DOI 10.1109/TAC.2022.3179539
View details for Web of Science ID 000873894800023
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SEQUENTIAL CONVEX PROGRAMMING FOR NON-LINEAR STOCHASTIC OPTIMAL CONTROL
ESAIM-CONTROL OPTIMISATION AND CALCULUS OF VARIATIONS
2022; 28
View details for DOI 10.1051/cocv/2022060
View details for Web of Science ID 000870425200001
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Safe Reinforcement Learning Using Black-Box Reachability Analysis
IEEE ROBOTICS AND AUTOMATION LETTERS
2022; 7 (4): 10665-10672
View details for DOI 10.1109/LRA.2022.3192205
View details for Web of Science ID 000838665800016
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< Convex Optimization for Trajectory Generation: A Tutorial on Generating Dynamically Feasible Trajectories Reliably and Efficiently
IEEE CONTROL SYSTEMS MAGAZINE
2022; 42 (5): 40-113
View details for DOI 10.1109/MCS.2022.3187542
View details for Web of Science ID 000861424700022
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A physics-based digital twin for model predictive control of autonomous unmanned aerial vehicle landing.
Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
2022; 380 (2229): 20210204
Abstract
This paper proposes a two-level, data-driven, digital twin concept for the autonomous landing of aircraft, under some assumptions. It features a digital twin instance (DTI) for model predictive control (MPC); and an innovative, real-time, digital twin prototype for fluid-structure interaction and flight dynamics to inform it. The latter digital twin is based on the linearization about a pre-designed glideslope trajectory of a high-fidelity, viscous, nonlinear computational model for flight dynamics; and its projection onto a low-dimensional approximation subspace to achieve real-time performance, while maintaining accuracy. Its main purpose is to predict in realtime, during flight, the state of an aircraft and the aerodynamic forces and moments acting on it. Unlike static lookup tables or regression-based surrogate models based on steady-state wind tunnel data, the aforementioned real-time digital twin prototype allows the DTI for MPC to be informed by a truly dynamic flight model, rather than a less accurate set of steady-state aerodynamic force and moment data points. The paper describes in detail the construction of the proposed two-level digital twin concept and its verification by numerical simulation. It also reports on its preliminary flight validation in autonomous mode for an off-the-shelf unmanned aerial vehicle instrumented at Stanford University. This article is part of the theme issue 'Data-driven prediction in dynamical systems'.
View details for DOI 10.1098/rsta.2021.0204
View details for PubMedID 35719063
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Routing and Rebalancing Intermodal Autonomous Mobility-on-Demand Systems in Mixed Traffic.
IEEE transactions on intelligent transportation systems : a publication of the IEEE Intelligent Transportation Systems Council
2022; 23 (8): 12263-12275
Abstract
This paper studies congestion-aware route-planning policies for intermodal Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility jointly with public transit under mixed traffic conditions (consisting of AMoD and private vehicles). First, we devise a network flow model to jointly optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. Second, we capture the effect of exogenous traffic stemming from private vehicles adapting to the AMoD flows in a user-centric fashion by leveraging a sequential approach. Since our results are in terms of link flows, we then provide algorithms to retrieve the explicit recommended routes to users. Finally, we showcase our framework with two case-studies considering the transportation sub-networks in Eastern Massachusetts and New York City, respectively. Our results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows. However, blending AMoD with public transit, walking and micromobility options can significantly improve the overall system performance by leveraging the high-throughput of public transit combined with the flexibility of walking and micromobility.
View details for DOI 10.1109/tits.2021.3112106
View details for PubMedID 37124136
View details for PubMedCentralID PMC10147341
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Backpropagation through signal temporal logic specifications: Infusing logical structure into gradient-based methods
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
2022
View details for DOI 10.1177/02783649221082115
View details for Web of Science ID 000802687000001
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Testing Gecko-Inspired Adhesives with Astrobee Aboard the International Space Station: Readying the Technology for Space
IEEE ROBOTICS & AUTOMATION MAGAZINE
2022
View details for DOI 10.1109/MRA.2022.3175597
View details for Web of Science ID 000805798200001
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Safe Active Dynamics Learning and Control: A Sequential Exploration-Exploitation Framework
IEEE TRANSACTIONS ON ROBOTICS
2022
View details for DOI 10.1109/TRO.2022.3154715
View details for Web of Science ID 000791719600001
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Integration of Reinforcement Learning in a Virtual Robotic Surgical Simulation.
Surgical innovation
2022: 15533506221095298
Abstract
Background. The revolutions in AI hold tremendous capacity to augment human achievements in surgery, but robust integration of deep learning algorithms with high-fidelity surgical simulation remains a challenge. We present a novel application of reinforcement learning (RL) for automating surgical maneuvers in a graphical simulation.Methods. In the Unity3D game engine, the Machine Learning-Agents package was integrated with the NVIDIA FleX particle simulator for developing autonomously behaving RL-trained scissors. Proximal Policy Optimization (PPO) was used to reward movements and desired behavior such as movement along desired trajectory and optimized cutting maneuvers along the deformable tissue-like object. Constant and proportional reward functions were tested, and TensorFlow analytics was used to informed hyperparameter tuning and evaluate performance.Results. RL-trained scissors reliably manipulated the rendered tissue that was simulated with soft-tissue properties. A desirable trajectory of the autonomously behaving scissors was achieved along 1 axis. Proportional rewards performed better compared to constant rewards. Cumulative reward and PPO metrics did not consistently improve across RL-trained scissors in the setting for movement across 2 axes (horizontal and depth).Conclusion. Game engines hold promising potential for the design and implementation of RL-based solutions to simulated surgical subtasks. Task completion was sufficiently achieved in one-dimensional movement in simulations with and without tissue-rendering. Further work is needed to optimize network architecture and parameter tuning for increasing complexity.
View details for DOI 10.1177/15533506221095298
View details for PubMedID 35503302
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View details for Web of Science ID 000380058900014
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Control of robotic mobility-on-demand systems: A queueing-theoretical perspective
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
2016; 35 (1-3): 186-203
View details for DOI 10.1177/0278364915581863
View details for Web of Science ID 000368032600011
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Free-Flyer Acquisition of Spinning Objects with Gecko-Inspired Adhesives
IEEE. 2016: 4907-4913
View details for Web of Science ID 000389516204032
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Model Predictive Control of Autonomous Mobility-on-Demand Systems
IEEE. 2016: 1382-1389
View details for Web of Science ID 000389516201035
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Simultaneous Model Identification and Task Satisfaction in the Presence of Temporal Logic Constraints
IEEE. 2016: 3682-3689
View details for Web of Science ID 000389516203030
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Risk Aversion in Finite Markov Decision Processes Using Total Cost Criteria and Average Value at Risk
IEEE. 2016: 335-342
View details for Web of Science ID 000389516200043
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Spacecraft Autonomy Challenges for Next-Generation Space Missions
SPRINGER-VERLAG BERLIN. 2016: 1-48
View details for DOI 10.1007/978-3-662-47694-9_1
View details for Web of Science ID 000385237300001
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Fast Marching Trees: A Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions
SPRINGER-VERLAG BERLIN. 2016: 667-684
View details for DOI 10.1007/978-3-319-28872-7_38
View details for Web of Science ID 000386325300038
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Real-Time, Propellant-Optimized Spacecraft Motion Planning under Clohessy-Wiltshire-Hill Dynamics
IEEE. 2016
View details for Web of Science ID 000388374902020
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Routing Autonomous Vehicles in Congested Transportation Networks: Structural Properties and Coordination Algorithms
MIT PRESS. 2016
View details for Web of Science ID 000570976500032
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Autonomous Calibration of MEMS Disk Resonating Gyroscope for Improved Sensor Performance
IEEE. 2016: 5803–10
View details for Web of Science ID 000388376105138
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Chance-constrained dynamic programming with application to risk-aware robotic space exploration
AUTONOMOUS ROBOTS
2015; 39 (4): 555-571
View details for DOI 10.1007/s10514-015-9467-7
View details for Web of Science ID 000362960600007
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Guest Editorial: Special issue on constrained decision-making in robotics
AUTONOMOUS ROBOTS
2015; 39 (4): 465-467
View details for DOI 10.1007/s10514-015-9489-1
View details for Web of Science ID 000362960600001
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Optimal Sampling-Based Motion Planning under Differential Constraints: the Drift Case with Linear Affine Dynamics.
Proceedings of the ... IEEE Conference on Decision & Control. IEEE Conference on Decision & Control
2015; 2015: 2574-2581
Abstract
In this paper we provide a thorough, rigorous theoretical framework to assess optimality guarantees of sampling-based algorithms for drift control systems: systems that, loosely speaking, can not stop instantaneously due to momentum. We exploit this framework to design and analyze a sampling-based algorithm (the Differential Fast Marching Tree algorithm) that is asymptotically optimal, that is, it is guaranteed to converge, as the number of samples increases, to an optimal solution. In addition, our approach allows us to provide concrete bounds on the rate of this convergence. The focus of this paper is on mixed time/control energy cost functions and on linear affine dynamical systems, which encompass a range of models of interest to applications (e.g., double-integrators) and represent a necessary step to design, via successive linearization, sampling-based and provably-correct algorithms for non-linear drift control systems. Our analysis relies on an original perturbation analysis for two-point boundary value problems, which could be of independent interest.
View details for PubMedID 26997749
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Fast marching tree: A fast marching sampling-based method for optimal motion planning in many dimensions
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
2015; 34 (7): 883-921
Abstract
In this paper we present a novel probabilistic sampling-based motion planning algorithm called the Fast Marching Tree algorithm (FMT*). The algorithm is specifically aimed at solving complex motion planning problems in high-dimensional configuration spaces. This algorithm is proven to be asymptotically optimal and is shown to converge to an optimal solution faster than its state-of-the-art counterparts, chiefly PRM* and RRT*. The FMT* algorithm performs a "lazy" dynamic programming recursion on a predetermined number of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-arrive space. As such, this algorithm combines features of both single-query algorithms (chiefly RRT) and multiple-query algorithms (chiefly PRM), and is reminiscent of the Fast Marching Method for the solution of Eikonal equations. As a departure from previous analysis approaches that are based on the notion of almost sure convergence, the FMT* algorithm is analyzed under the notion of convergence in probability: the extra mathematical flexibility of this approach allows for convergence rate bounds-the first in the field of optimal sampling-based motion planning. Specifically, for a certain selection of tuning parameters and configuration spaces, we obtain a convergence rate bound of order O(n-1/d+ρ), where n is the number of sampled points, d is the dimension of the configuration space, and ρ is an arbitrarily small constant. We go on to demonstrate asymptotic optimality for a number of variations on FMT*, namely when the configuration space is sampled non-uniformly, when the cost is not arc length, and when connections are made based on the number of nearest neighbors instead of a fixed connection radius. Numerical experiments over a range of dimensions and obstacle configurations confirm our the-oretical and heuristic arguments by showing that FMT*, for a given execution time, returns substantially better solutions than either PRM* or RRT*, especially in high-dimensional configuration spaces and in scenarios where collision-checking is expensive.
View details for DOI 10.1177/0278364915577958
View details for Web of Science ID 000355612400004
View details for PubMedCentralID PMC4798023
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Fast Marching Tree: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions.
The International journal of robotics research
2015; 34 (7): 883-921
Abstract
In this paper we present a novel probabilistic sampling-based motion planning algorithm called the Fast Marching Tree algorithm (FMT*). The algorithm is specifically aimed at solving complex motion planning problems in high-dimensional configuration spaces. This algorithm is proven to be asymptotically optimal and is shown to converge to an optimal solution faster than its state-of-the-art counterparts, chiefly PRM* and RRT*. The FMT* algorithm performs a "lazy" dynamic programming recursion on a predetermined number of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-arrive space. As such, this algorithm combines features of both single-query algorithms (chiefly RRT) and multiple-query algorithms (chiefly PRM), and is reminiscent of the Fast Marching Method for the solution of Eikonal equations. As a departure from previous analysis approaches that are based on the notion of almost sure convergence, the FMT* algorithm is analyzed under the notion of convergence in probability: the extra mathematical flexibility of this approach allows for convergence rate bounds-the first in the field of optimal sampling-based motion planning. Specifically, for a certain selection of tuning parameters and configuration spaces, we obtain a convergence rate bound of order O(n-1/d+ρ), where n is the number of sampled points, d is the dimension of the configuration space, and ρ is an arbitrarily small constant. We go on to demonstrate asymptotic optimality for a number of variations on FMT*, namely when the configuration space is sampled non-uniformly, when the cost is not arc length, and when connections are made based on the number of nearest neighbors instead of a fixed connection radius. Numerical experiments over a range of dimensions and obstacle configurations confirm our the-oretical and heuristic arguments by showing that FMT*, for a given execution time, returns substantially better solutions than either PRM* or RRT*, especially in high-dimensional configuration spaces and in scenarios where collision-checking is expensive.
View details for DOI 10.1177/0278364915577958
View details for PubMedID 27003958
View details for PubMedCentralID PMC4798023
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Trading Safety Versus Performance: Rapid Deployment of Robotic Swarms With Robust Performance Constraints
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
2015; 137 (3)
View details for DOI 10.1115/1.4028117
View details for Web of Science ID 000349754500007
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Optimal Sampling-Based Motion Planning under Differential Constraints: the Driftless Case.
IEEE International Conference on Robotics and Automation : ICRA : [proceedings]. IEEE International Conference on Robotics and Automation
2015; 2015: 2368–75
Abstract
Motion planning under differential constraints is a classic problem in robotics. To date, the state of the art is represented by sampling-based techniques, with the Rapidly-exploring Random Tree algorithm as a leading example. Yet, the problem is still open in many aspects, including guarantees on the quality of the obtained solution. In this paper we provide a thorough theoretical framework to assess optimality guarantees of sampling-based algorithms for planning under differential constraints. We exploit this framework to design and analyze two novel sampling-based algorithms that are guaranteed to converge, as the number of samples increases, to an optimal solution (namely, the Differential Probabilistic RoadMap algorithm and the Differential Fast Marching Tree algorithm). Our focus is on driftless control-affine dynamical models, which accurately model a large class of robotic systems. In this paper we use the notion of convergence in probability (as opposed to convergence almost surely): the extra mathematical flexibility of this approach yields convergence rate bounds - a first in the field of optimal sampling-based motion planning under differential constraints. Numerical experiments corroborating our theoretical results are presented and discussed.
View details for PubMedID 26618041
View details for PubMedCentralID PMC4659485
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Toward a Real-Time Framework for Solving the Kinodynamic Motion Planning Problem
IEEE COMPUTER SOC. 2015: 928-934
View details for Web of Science ID 000370974900134
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Decentralized Algorithms for 3D Symmetric Formations in Robotic Networks - a Contraction Theory Approach
IEEE COMPUTER SOC. 2015: 1274-1281
View details for Web of Science ID 000370974901041
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A Queueing Network Approach to the Analysis and Control of Mobility-On-Demand Systems
IEEE. 2015: 4702-4709
View details for Web of Science ID 000370259204132
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Models, Algorithms, and Evaluation for Autonomous Mobility-On-Demand Systems
IEEE. 2015: 2573-2587
View details for Web of Science ID 000370259202110
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A SAMPLING-BASED APPROACH TO SPACECRAFT AUTONOMOUS MANEUVERING WITH SAFETY SPECIFICATIONS
UNIVELT INC. 2015: 725-737
View details for Web of Science ID 000371649600054
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A Convex Optimization Approach to Smooth Trajectories for Motion Planning with Car-Like Robots
IEEE. 2015: 835-842
View details for Web of Science ID 000381554501022
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Risk-Sensitive and Robust Decision-Making: a CVaR Optimization Approach
NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2015
View details for Web of Science ID 000450913103052
- A Unifying Framework for Time-Consistent, Risk-Averse Model Predictive Control: Theory and Algorithms 2014
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A Dynamical Characterization of Internally-Actuated Microgravity Mobility Systems
IEEE. 2014: 6618-6624
View details for Web of Science ID 000377221106099
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Rapid Multirobot Deployment with Time Constraints
IEEE. 2014: 1147-1154
View details for Web of Science ID 000349834601036
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Distributed consensus with mixed time/communication bandwidth performance metrics
IEEE. 2014: 286-293
View details for Web of Science ID 000380426900042
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On the Fundamental Limitations of Performance for Distributed Decision-Making in Robotic Networks
IEEE. 2014: 2433-2440
View details for Web of Science ID 000370073802093
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A Machine Learning Approach for Real-Time Reachability Analysis
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
IEEE. 2014: 2202–2208
View details for Web of Science ID 000349834602048
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A Framework for Time-Consistent, Risk-Averse Model Predictive Control: Theory and Algorithms
American Control Conference
IEEE. 2014: 4204–4211
View details for Web of Science ID 000346492604129
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Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore
2nd Annual Workshop on Road Vehicle Automation
SPRINGER INT PUBLISHING AG. 2014: 229–245
View details for DOI 10.1007/978-3-319-05990-7_20
View details for Web of Science ID 000345580200020
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Asymptotically Optimal Algorithms for One-to-One Pickup and Delivery Problems With Applications to Transportation Systems
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2013; 58 (9): 2261-2276
View details for DOI 10.1109/TAC.2013.2259993
View details for Web of Science ID 000323568500009
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Spacecraft/Rover Hybrids for the Exploration of Small Solar System Bodies
IEEE Aerospace Conference
IEEE. 2013
View details for Web of Science ID 000320123902023
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A Uniform-Grid Discretization Algorithm for Stochastic Optimal Control with Risk Constraints
IEEE. 2013: 2470-2475
View details for Web of Science ID 000352223502129
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Decentralized decision-making on robotic networks with hybrid performance metrics
51st Annual Allerton Conference on Communication, Control, and Computing
IEEE. 2013: 358–365
View details for Web of Science ID 000350802400050
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Internally-Actuated Rovers for All-Access Surface Mobility: Theory and Experimentation
IEEE International Conference on Robotics and Automation (ICRA)
IEEE. 2013: 5481–5488
View details for Web of Science ID 000337617305075
- Internally-Actuated Rovers for All-Access Surface Mobility: Theory and Experimentation 2013
- Guidance, Navigation, and Control Technology Assessment for Future Planetary Science Missions. Technical Report for Planetary Science Division, Science Mission Directorate, NASA 2013
- Decentralized decision-making on robotic networks with hybrid performance metrics 2013
- A Uniform-grid Discretization Algorithm for Stochastic Optimal Control with Risk Constraints 2013
- Asymptotically Optimal Algorithms for Pickup and Delivery Problems with Application to Large-Scale Transportation Systems IEEE Transactions on Automatic Control 2013
- Rebalancing the Rebalancers: Optimally Routing Vehicles and Drivers in Mobility-on-Demand Systems 2013
- Stochastic Optimal Control With Dynamic, Time-Consistent Risk Constraints 2013
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Stochastic Optimal Control With Dynamic, Time-Consistent Risk Constraints
American Control Conference (ACC)
IEEE. 2013: 390–395
View details for Web of Science ID 000327210200065
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Rebalancing the Rebalancers: Optimally Routing Vehicles and Drivers in Mobility-on-Demand Systems
American Control Conference (ACC)
IEEE. 2013: 2362–2367
View details for Web of Science ID 000327210202087
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Robotic load balancing for mobility-on-demand systems
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH
2012; 31 (7): 839-854
View details for DOI 10.1177/0278364912444766
View details for Web of Science ID 000304699300004
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Cost Bounds for Pickup and Delivery Problems with Application to Large-Scale Transportation Systems
American Control Conference (ACC)
IEEE COMPUTER SOC. 2012: 2120–2127
View details for Web of Science ID 000310776202068
- Models and Asymptotically Optimal Algorithms for Pickup and Delivery Problems on Roadmaps 2012
- Observational Strategies for the Exploration of Small Solar System Bodies 2012
- A Risk-Constrained Multi-Stage Decision Making Approach to the Architectural Analysis of Mars Missions 2012
- Spacecraft/Rover Hybrids for the Exploration of Small Solar System Bodies. Final Report for NASA NIAC 2011 Program. 2012
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A Risk-Constrained Multi-Stage Decision Making Approach to the Architectural Analysis of Planetary Missions
51st IEEE Annual Conference on Decision and Control (CDC)
IEEE. 2012: 2102–2109
View details for Web of Science ID 000327200402078
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Models and Efficient Algorithms for Pickup and Delivery Problems on Roadmaps
51st IEEE Annual Conference on Decision and Control (CDC)
IEEE. 2012: 5691–5698
View details for Web of Science ID 000327200405158
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Dynamic Vehicle Routing for Robotic Systems
PROCEEDINGS OF THE IEEE
2011; 99 (9): 1482-1504
View details for DOI 10.1109/JPROC.2011.2158181
View details for Web of Science ID 000294126300004
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Distributed Algorithms for Environment Partitioning in Mobile Robotic Networks
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2011; 56 (8): 1834-1848
View details for DOI 10.1109/TAC.2011.2112410
View details for Web of Science ID 000293750600007
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Adaptive and Distributed Algorithms for Vehicle Routing in a Stochastic and Dynamic Environment
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
2011; 56 (6): 1259-1274
View details for DOI 10.1109/TAC.2010.2092850
View details for Web of Science ID 000291430200003
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An Asymptotically Optimal Algorithm for Pickup and Delivery Problems
50th IEEE Conference of Decision and Control (CDC)/European Control Conference (ECC)
IEEE. 2011: 584–590
View details for Web of Science ID 000303506201029
- Load Balancing for Mobility-on-Demand Systems 2011
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Distributed Control of Spacecraft Formations via Cyclic Pursuit: Theory and Experiments
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
2010; 33 (5): 1655-1669
View details for DOI 10.2514/1.46511
View details for Web of Science ID 000282073600030
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DYNAMIC VEHICLE ROUTING WITH PRIORITY CLASSES OF STOCHASTIC DEMANDS
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
2010; 48 (5): 3224-3245
View details for DOI 10.1137/090749347
View details for Web of Science ID 000277585500002
- Fundamental Performance Limits and Efficient Policies for Transportation-On-Demand Systems 2010
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Dynamic Vehicle Routing with Stochastic Time Constraints
IEEE International Conference on Robotics and Automation (ICRA)
IEEE. 2010: 1460–1467
View details for Web of Science ID 000284150000098
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Fundamental Performance Limits and Efficient Polices for Transportation-On-Demand Systems
49th IEEE Conference on Decision and Control (CDC)
IEEE. 2010: 5622–5629
View details for Web of Science ID 000295049106063
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A Stochastic and Dynamic Vehicle Routing Problem with Time Windows and Customer Impatience
1st International Conference on Robot Communication and Coordination (ROBOCOMM 2007)
SPRINGER. 2009: 350–64
View details for DOI 10.1007/s11036-008-0101-1
View details for Web of Science ID 000265045000008
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Sharing the Load Mobile Robotic Networks in Dynamic Environments
IEEE ROBOTICS & AUTOMATION MAGAZINE
2009; 16 (2): 52-61
View details for DOI 10.1109/MRA.2009.932528
View details for Web of Science ID 000267126600009
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Equitable Partitioning Policies for Robotic Networks
IEEE International Conference on Robotics and Automation
IEEE. 2009: 3979–3984
View details for Web of Science ID 000276080402023
- Sharing the load IEEE Robotics & Automation Magazine 2009; 16 (2): 52-61
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Distributed Control of Spacecraft Formation via Cyclic Pursuit: Theory and Experiments
American Control Conference 2009
IEEE. 2009: 4811–4817
View details for Web of Science ID 000270044902138
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Dynamic Multi-Vehicle Routing with Multiple Classes of Demands
American Control Conference 2009
IEEE. 2009: 604–609
View details for Web of Science ID 000270044900099
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Distributed Policies for Equitable Partitioning: Theory and Applications
47th IEEE Conference on Decision and Control
IEEE. 2008: 4191–4197
View details for Web of Science ID 000307311604052
- Dynamic vehicle routing with heterogeneous demands 2008
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Decentralized policies for geometric pattern formation and path coverage
JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME
2007; 129 (5): 633-643
View details for DOI 10.1115/1.2767658
View details for Web of Science ID 000249705300007
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Decentralized policies for geometric pattern formation
26th American Control Conference
IEEE. 2007: 5823–5828
View details for Web of Science ID 000252258804102
- Decentralized algorithms for stochastic and dynamic vehicle routing with general demand distribution 2007
- Decentralized Vehicle Routing in a Stochastic and Dynamic Environment with Customer Impatience 2007
- Climbing Obstacle in Bio-robots via CNN and Adaptive Attitude Control International Journal of Circuit Theory and Applications 2006; 34 (1): 109-125
- An innovative mechanical and control architecture for a biomimetic hexapod for planetary exploration Space Technology 2006; 26 (1-2): 13-24
- Realization of a CNN-Driven Cockroach-Inspired Robot 2006
- Towards autonomous adaptive behavior in a bio-inspired CNN-controlled robot 2006
- An innovative mechanical and control architecture for a biomimetic hexapod for planetary exploration 2005
- Climbing Obstacles via Bio-Inspired CNN-CPG and Adaptive Attitude Control 2005
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An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning.
Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE/RSJ International Conference on Intelligent Robots and Systems
; 2015: 2072–78
Abstract
Bi-directional search is a widely used strategy to increase the success and convergence rates of sampling-based motion planning algorithms. Yet, few results are available that merge both bi-directional search and asymptotic optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The objective of this paper is to fill this gap. Specifically, this paper presents a bi-directional, sampling-based, asymptotically-optimal algorithm named Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*) algorithm to bidirectional search while preserving its key properties, chiefly lazy search and asymptotic optimality through convergence in probability. BFMT* performs a two-source, lazy dynamic programming recursion over a set of randomly-drawn samples, correspondingly generating two search trees: one in cost-to-come space from the initial configuration and another in cost-to-go space from the goal configuration. Numerical experiments illustrate the advantages of BFMT* over its unidirectional counterpart, as well as a number of other state-of-the-art planners.
View details for PubMedID 27004130
View details for PubMedCentralID PMC4797999