# Safe Navigation in Uncertain Crowded Environments Using Risk Adaptive CVaR Barrier Functions

arXiv: https://arxiv.org/abs/2504.06513 · HTML: https://mlsanity.com/paper/2504.06513/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2025.

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

- [DiffCVaR: Reinforcement Learning for Risk Adaptation via Differentiable CVaR Barrier Functions](https://mlsanity.com/paper/2605.21257/) (2026)
- [KinematicRL: A Sim-to-Real Reinforcement Learning Framework For Social Navigation With Kinodynamic Feasibility](https://mlsanity.com/paper/2606.12042/) (2026)

## Abstract (arXiv)

Robot navigation in dynamic, crowded environments poses a significant challenge due to the inherent uncertainties in the obstacle model. In this work, we propose a risk-adaptive approach based on the Conditional Value-at-Risk Barrier Function (CVaR-BF), where the risk level is automatically adjusted to accept the minimum necessary risk, achieving a good performance in terms of safety and optimization feasibility under uncertainty. Additionally, we introduce a dynamic zone-based barrier function which characterizes the collision likelihood by evaluating the relative state between the robot and the obstacle. By integrating risk adaptation with this new function, our approach adaptively expands the safety margin, enabling the robot to proactively avoid obstacles in highly dynamic environments. Comparisons and ablation studies demonstrate that our method outperforms existing social navigation approaches, and validate the effectiveness of our proposed framework.