# Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation

arXiv: https://arxiv.org/abs/2305.13467 · HTML: https://mlsanity.com/paper/2305.13467/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 25 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 13 of 13)

- [Control Barrier Functions: Theory and Applications](https://mlsanity.com/paper/1903.11199/) (2019)
- [Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function](https://mlsanity.com/paper/2007.11718/) (2020)
- [Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates](https://mlsanity.com/paper/1912.09957/) (2019)
- [Probabilistic Safety-Assured Adaptive Merging Control for Autonomous Vehicles](https://mlsanity.com/paper/2104.14159/) (2021)
- [Rule-Based Safety-Critical Control Design using Control Barrier Functions with Application to Autonomous Lane Change](https://mlsanity.com/paper/2103.12382/) (2021)
- [Risk-Aware Submodular Optimization for Multi-Robot Coordination](https://mlsanity.com/paper/2003.10492/) (2020)
- [Sufficient Conditions for Feasibility of Optimal Control Problems Using Control Barrier Functions](https://mlsanity.com/paper/2011.08248/) (2020)
- [Safe Control for Nonlinear Systems with Stochastic Uncertainty via Risk Control Barrier Functions](https://mlsanity.com/paper/2203.15892/) (2022)
- [Decentralized Probabilistic Multi-Robot Collision Avoidance Using Buffered Uncertainty-Aware Voronoi Cells](https://mlsanity.com/paper/2201.04012/) (2022)
- [Adaptive Safe Merging Control for Heterogeneous Autonomous Vehicles using Parametric Control Barrier Functions](https://mlsanity.com/paper/2202.09936/) (2022)
- [Responsibility-associated Multi-agent Collision Avoidance with Social Preferences](https://mlsanity.com/paper/2206.09030/) (2022)
- [Decentralized Risk-Aware Tracking of Multiple Targets](https://mlsanity.com/paper/2208.02772/) (2022)
- [Safety Barrier Certificates for Heterogeneous Multi-Robot Systems](https://mlsanity.com/paper/1609.00651/) (2016)

## Built on by (most cited first, 2 of 2)

- [PC-Diffuser: Path-Consistent Capsule CBF Safety Filtering for Diffusion-Based Trajectory Planner](https://mlsanity.com/paper/2603.10330/) (2026)
- [SafeDiSTL: Decomposing Joint Constraints for Safe Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2610.13737/) (2026)

## Abstract (arXiv)

Decentralized control schemes are increasingly favored in various domains that involve multi-agent systems due to the need for computational efficiency as well as general applicability to large-scale systems. However, in the absence of an explicit global coordinator, it is hard for distributed agents to determine how to efficiently interact with others. In this paper, we present a risk-aware decentralized control framework that provides guidance on how much relative responsibility share (a percentage) an individual agent should take to avoid collisions with others while moving efficiently without direct communications. We propose a novel Control Barrier Function (CBF)-inspired risk measurement to characterize the aggregate risk agents face from potential collisions under motion uncertainty. We use this measurement to allocate responsibility shares among agents dynamically and develop risk-aware decentralized safe controllers. In this way, we are able to leverage the flexibility of robots with lower risk to improve the motion flexibility for those with higher risk, thus achieving improved collective safety. We demonstrate the validity and efficiency of our proposed approach through two examples: ramp merging in autonomous driving and a multi-agent position-swapping game.