Jonathan P. How

MADER: Trajectory Planner in Multi-Agent and Dynamic Environments

Oct 21, 2020
Jesus Tordesillas, Jonathan P. How

* 14 pages, 15 figures

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MINVO Basis: Finding Simplexes with Minimum Volume Enclosing Polynomial Curves

Oct 21, 2020
Jesus Tordesillas, Jonathan P. How

* 17 pages, 16 figures

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Robustness Analysis of Neural Networks via Efficient Partitioning: Theory and Applications in Control Systems

Oct 01, 2020
Michael Everett, Golnaz Habibi, Jonathan P. How

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Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection

Jul 15, 2020
Lena M. Downes, Ted J. Steiner, Jonathan P. How

* 6 pages, 4 figures. This work was accepted by the 2020 American Control Conference

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Set-Invariant Constrained Reinforcement Learning with a Meta-Optimizer

Jul 09, 2020
Chuangchuang Sun, Dong-Ki Kim, Jonathan P. How

* Accepted to ICML 2020 Workshop Theoretical Foundations of RL

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Collision Probabilities for Continuous-Time Systems Without Sampling [with Appendices]

Jun 01, 2020
Kristoffer M. Frey, Ted J. Steiner, Jonathan P. How

* To appear at RSS 2020

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Certified Adversarial Robustness for Deep Reinforcement Learning

Apr 11, 2020
Michael Everett, Bjorn Lutjens, Jonathan P. How

* arXiv admin note: text overlap with arXiv:1910.12908

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Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments

Mar 10, 2020
Stewart Jamieson, Jonathan P. How, Yogesh Girdhar

* 7 pages, 4 figures; accepted for presentation in IEEE Int. Conf. on Robotics and Automation, ICRA '20, Paris, France, June 2020

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Asynchronous and Parallel Distributed Pose Graph Optimization

Mar 06, 2020
Yulun Tian, Alec Koppel, Amrit Singh Bedi, Jonathan P. How

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Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor

Mar 04, 2020
Andrea Tagliabue, Aleix Paris, Suhan Kim, Regan Kubicek, Sarah Bergbreiter, Jonathan P. How

* The first two authors contributed equally

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A Distributed Pipeline for Scalable, Deconflicted Formation Flying

Mar 04, 2020
Parker C. Lusk, Xiaoyi Cai, Samir Wadhwania, Aleix Paris, Kaveh Fathian, Jonathan P. How

* 8 pages, 8 figures, submitted to 2020 IEEE IROS with IEEE RAS RA-L option

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Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph

Mar 03, 2020
Chuangchuang Sun, Macheng Shen, Jonathan P. How

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R-MADDPG for Partially Observable Environments and Limited Communication

Feb 18, 2020
Rose E. Wang, Michael Everett, Jonathan P. How

* Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 36th International Conference on Machine Learning, Long Beach, California, USA, 2019
* Reinforcement Learning for Real Life (RL4RealLife) Workshop in the 36th International Conference on Machine Learning, Long Beach, California, USA, 2019

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Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

Jan 18, 2020
Samaneh Hosseini Semnani, Hugh Liu, Michael Everett, Anton de Ruiter, Jonathan P. How

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FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments

Jan 09, 2020
Jesus Tordesillas, Brett T. Lopez, Michael Everett, Jonathan P. How

* Journal paper. arXiv admin note: text overlap with arXiv:1903.03558

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Block-Coordinate Descent on the Riemannian Staircase for Certifiably Correct Distributed Rotation and Pose Synchronization

Dec 21, 2019
Yulun Tian, Kasra Khosoussi, Jonathan P. How

* technical report version 3 (add missing step in algorithm 1)

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Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning

Sep 19, 2019
Arpan Kusari, Jonathan P. How

* Submitted to ICRA 2020

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Jonathan P. How

MADER: Trajectory Planner in Multi-Agent and Dynamic Environments

MINVO Basis: Finding Simplexes with Minimum Volume Enclosing Polynomial Curves

Robustness Analysis of Neural Networks via Efficient Partitioning: Theory and Applications in Control Systems

Lunar Terrain Relative Navigation Using a Convolutional Neural Network for Visual Crater Detection

Set-Invariant Constrained Reinforcement Learning with a Meta-Optimizer

Collision Probabilities for Continuous-Time Systems Without Sampling [with Appendices]

Certified Adversarial Robustness for Deep Reinforcement Learning

Active Reward Learning for Co-Robotic Vision Based Exploration in Bandwidth Limited Environments

Asynchronous and Parallel Distributed Pose Graph Optimization

Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor

A Distributed Pipeline for Scalable, Deconflicted Formation Flying

Scaling Up Multiagent Reinforcement Learning for Robotic Systems: Learn an Adaptive Sparse Communication Graph

R-MADDPG for Partially Observable Environments and Limited Communication

Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

FASTER: Fast and Safe Trajectory Planner for Flights in Unknown Environments

Block-Coordinate Descent on the Riemannian Staircase for Certifiably Correct Distributed Rotation and Pose Synchronization

Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections

Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Games

Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning

Predicting optimal value functions by interpolating reward functions in scalarized multi-objective reinforcement learning

Search and Rescue under the Forest Canopy using Multiple UAVs

Towards Online Observability-Aware Trajectory Optimization for Landmark-based Estimators

Planning Beyond the Sensing Horizon Using a Learned Context