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We study the multi-agent safe control problem where agents should avoid collisions to static obstacles and collisions with each other while reaching their goals.
A time-dependent hamilton-jacobi formulation of reachable sets for continuous dynamic games
Ian M Mitchell, Alexandre M Bayen, and Claire J Tomlin · 2005
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A framework for worst-case and stochastic safety verification using barrier certificates
Stephen Prajna, Ali Jadbabaie, and George J Pappas · 2007
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Constructive safety using control barrier functions
Peter Wieland and Frank Allgöwer · 2007
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Reciprocal velocity obstacles for real-time multi-agent navigation
Jur Van den Berg, Ming Lin, and Dinesh Manocha · 2008
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Smoothness, low noise and fast rates
Nathan Srebro, Karthik Sridharan, and Ambuj Tewari · 2010
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Optimal reciprocal collision avoidance for multiple non-holonomic robots
Javier Alonso-Mora, Andreas Breitenmoser, Martin Rufli, Paul Beardsley, and Roland Siegwart · 2013
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Control barrier function based quadratic programs with application to adaptive cruise control
Aaron D Ames, Jessy W Grizzle, and Paulo Tabuada · 2014
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Trajectory tracking with collision avoidance for nonholonomic vehicles with acceleration constraints and limited sensing
Erick J. Rodríguez-Seda, Chinpei Tang, Mark W. Spong, and Dušan M. Stipanović · 2014
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Control barrier certificates for safe swarm behavior
Urs Borrmann, Li Wang, Aaron D Ames, and Magnus Egerstedt · 2015
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Safe, multi-agent, reinforcement learning for autonomous driving
Shai Shalev-Shwartz, Shaked Shammah, and Amnon Shashua · 2016
Earlier work this paper cites.
Control barrier function based quadratic programs for safety critical systems
Aaron D Ames, Xiangru Xu, Jessy W Grizzle, and Paulo Tabuada · 2017
Earlier work this paper cites.
Nonsmooth barrier functions with applications to multi-robot systems
Paul Glotfelter, Jorge Cortés, and Magnus Egerstedt · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Ryan Lowe, Yi I Wu, Aviv Tamar, Jean Harb, OpenAI Pieter Abbeel, and Igor Mordatch · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R. Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas · 2017
Cited alongside, same era.
Safety barrier certificates for collisions-free multirobot systems
Li Wang, Aaron D Ames, and Magnus Egerstedt · 2017
Cited alongside, same era.
Correctness guarantees for the composition of lane keeping and adaptive cruise control
Xiangru Xu, Jessy W Grizzle, Paulo Tabuada, and Aaron D Ames · 2017
Cited alongside, same era.
Magent: A many-agent reinforcement learning platform for artificial collective intelligence
Lianmin Zheng, Jiacheng Yang, Han Cai, Weinan Zhang, Jun Wang, and Yong Yu · 2017
Cited alongside, same era.
Motion planning among dynamic, decision-making agents with deep reinforcement learning
Michael Everett, Yu Fan Chen, and Jonathan P How · 2018
Cited alongside, same era.
Guaranteed obstacle avoidance for multi-robot operations with limited actuation: a control barrier function approach
Yuxiao Chen, Andrew Singletary, and Aaron D Ames · 2020
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Safe multi-agent interaction through robust control barrier functions with learned uncertainties
Richard Cheng, Mohammad Javad Khojasteh, Aaron D Ames, and Joel W Burdick · 2020
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Jason Choi, Fernando Castañeda, Claire J Tomlin, and Koushil Sreenath · 2020
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Fast and guaranteed safe controller synthesis for nonlinear vehicle models
Chuchu Fan, Kristina Miller, and Sayan Mitra · 2020
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Safe learning of quadrotor dynamics using barrier certificates
Li Wang, Evangelos A Theodorou, and Magnus Egerstedt · 2018
Cited alongside, same era.
Fully decentralized multi-agent reinforcement learning with networked agents
Kaiqing Zhang, Zhuoran Yang, Han Liu, Tong Zhang, and Tamer Basar · 2018
Cited alongside, same era.
Neural lyapunov control
Ya-Chien Chang, Nima Roohi, and Sicun Gao · 2019
Cited alongside, same era.
End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks
Richard Cheng, Gábor Orosz, Richard M Murray, and Joel W Burdick · 2019
Cited alongside, same era.
Searching with consistent prioritization for multi-agent path finding
Hang Ma, Daniel Harabor, Peter. J Stuckey, Jiaoyang Li, and Sven Koenig · 2019
Cited alongside, same era.
Joint optimization of multi-UAV target assignment and path planning based on multi-agent reinforcement learning
Han Qie, Dianxi Shi, Tianlong Shen, Xinhai Xu, Yuan Li, and Liujing Wang · 2019
Cited alongside, same era.
Mamps: Safe multi-agent reinforcement learning via model predictive shielding
Wenbo Zhang and Osbert Bastani · 2019
Cited alongside, same era.
Wanxin Jin, Zhaoran Wang, Zhuoran Yang, and Shaoshuai Mou · 2020
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Pic: permutation invariant critic for multi-agent deep reinforcement learning
Iou-Jen Liu, Raymond A Yeh, and Alexander G Schwing · 2020
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Learning control barrier functions from expert demonstrations
Alexander Robey, Haimin Hu, Lars Lindemann, Hanwen Zhang, Dimos V Dimarogonas, Stephen Tu, and Nikolai Matni · 2020
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Learning barrier functions for constrained motion planning with dynamical systems
Matteo Saveriano and Dongheui Lee · 2020
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Synthesis of control barrier functions using a supervised machine learning approach
Mohit Srinivasan, Amogh Dabholkar, Samuel Coogan, and Patricio Vela · 2020
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Learning for safety-critical control with control barrier functions
Andrew Taylor, Andrew Singletary, Yisong Yue, and Aaron Ames · 2020
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Scalable and safe multi-agent motion planning with nonlinear dynamics and bounded disturbances
Jingkai Chen, Jiaoyang Li, Chuchu Fan, and Brian C. Williams · 2021
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Closing the closed-loop distribution shift in safe imitation learning
Stephen Tu, Alexander Robey, and Nikolai Matni · 2021
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