mlsanityData to 7 Oct 2026

Online Control Barrier Functions for Decentralized Multi-Agent Navigation

arXiv 2303.04313 ↗First posted 8 Mar 2023cs.RO cs.LG cs.MAcited by 10

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Abstract (arXiv)

Control barrier functions (CBFs) enable guaranteed safe multi-agent navigation in the continuous domain. The resulting navigation performance, however, is highly sensitive to the underlying hyperparameters. Traditional approaches consider fixed CBFs (where parameters are tuned apriori), and hence, typically do not perform well in cluttered and highly dynamic environments: conservative parameter values can lead to inefficient agent trajectories, or even failure to reach goal positions, whereas aggressive parameter values can lead to infeasible controls. To overcome these issues, in this paper, we propose online CBFs, whereby hyperparameters are tuned in real-time, as a function of what agents perceive in their immediate neighborhood. Since the explicit relationship between CBFs and navigation performance is hard to model, we leverage reinforcement learning to learn CBF-tuning policies in a model-free manner. Because we parameterize the policies with graph neural networks (GNNs), we are able to synthesize decentralized agent controllers that adjust parameter values locally, varying the degree of conservative and aggressive behaviors across agents. Simulations as well as real-world experiments show that (i) online CBFs are capable of solving navigation scenarios that are infeasible for fixed CBFs, and (ii), that they improve navigation performance by adapting to other agents and changes in the environment.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 26 of its 50 references that are in the map. First posted on arXiv 8 Mar 2023.

Builds on

26 of 50 references.

References · 26

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2017Graph Attention Networkscited by 8,854
  3. 2018On Evaluation of Embodied Navigation Agentscited by 526
  4. 2022Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methodscited by 148
  5. 2019A Review of Cooperative Multi-Agent Deep Reinforcement Learningcited by 147
  6. 2020Training robust neural networks using Lipschitz boundscited by 98
  7. 2021Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functionscited by 91
  8. 2020Learning Control Barrier Functions from Expert Demonstrationscited by 88
  9. 2019Graph Neural Networks for Decentralized Multi-Robot Path Planningcited by 84
  10. 2021Learning Safe Multi-Agent Control with Decentralized Neural Barrier Certificatescited by 71
  11. 2019Learning Decentralized Controllers for Robot Swarms with Graph Neural Networkscited by 62
  12. 2020Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Functioncited by 42
14 more
  1. 2019Control Barrier Functions for Systems with High Relative Degreecited by 30
  2. 2016Robustness of Control Barrier Functions for Safety Critical Controlcited by 29
  3. 2019Adaptive Safety with Control Barrier Functionscited by 25
  4. 2020Safe Multi-Agent Interaction through Robust Control Barrier Functions with Learned Uncertaintiescited by 20
  5. 2021Wide and Deep Graph Neural Network with Distributed Online Learningcited by 14
  6. 2019Safe Policy Synthesis in Multi-Agent POMDPs via Discrete-Time Barrier Functionscited by 11
  7. 2021Enhancing Feasibility and Safety of Nonlinear Model Predictive Control with Discrete-Time Control Barrier Functionscited by 8
  8. 2022Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigationcited by 8
  9. 2018Control of Multi-Agent Systems with Finite Time Control Barrier Certificates and Temporal Logiccited by 6
  10. 2022Environment Optimization for Multi-Agent Navigationcited by 6
  11. 2021Control Barrier Functions in Sampled-Data Systemscited by 5
  12. 2020Combining Control Barrier Functions and Behavior Trees for Multi-Agent Underwater Coverage Missionscited by 3
  13. 2019Explicit Motion Risk Representationcited by 2
  14. 2022Feasibility Guaranteed Traffic Merging Control Using Control Barrier Functionscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 3; 2026: 2.

10 papers.

  1. 2024Co-Optimizing Reconfigurable Environments and Policies for Decentralized Multi-Agent Navigationcited by 4
  2. 2025Hierarchical Multi-Agent Reinforcement Learning with Control Barrier Functions for Safety-Critical Autonomous Systemscited by 4
  3. 2024Learning Control Barrier Functions and their application in Reinforcement Learning: A Surveycited by 3
  4. 2023On the Trade-Off between Stability and Representational Capacity in Graph Neural Networkscited by 2
  5. 2023Model-Assisted Probabilistic Safe Adaptive Control With Meta-Bayesian Learningcited by 1
  6. 2024Safety-Aware Multi-Agent Learning for Dynamic Network Bridgingcited by 0
  7. 2025ReCoDe: Reinforcement Learning-based Dynamic Constraint Design for Multi-Agent Coordinationcited by 0
  8. 2025LEARN: Learning End-to-End Aerial Resource-Constrained Multi-Robot Navigationcited by 0
  9. 2026GIANT - Global Path Integration and Attentive Graph Networks for Multi-Agent Trajectory Planningcited by 0
  10. 2026Stochastic Sequential Decision Making over Expanding Networks with Graph Filteringcited by 0