# Closing the Perception-Action Loop for Semantically Safe Navigation in Semi-Static Environments

arXiv: https://arxiv.org/abs/2404.14546 · HTML: https://mlsanity.com/paper/2404.14546/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2024.

## Built on by (most cited first, 1 of 1)

- [Perpetua: Multi-Hypothesis Persistence Modeling for Semi-Static Environments](https://mlsanity.com/paper/2507.18808/) (2025)

## Abstract (arXiv)

Autonomous robots navigating in changing environments demand adaptive navigation strategies for safe long-term operation. While many modern control paradigms offer theoretical guarantees, they often assume known extrinsic safety constraints, overlooking challenges when deployed in real-world environments where objects can appear, disappear, and shift over time. In this paper, we present a closed-loop perception-action pipeline that bridges this gap. Our system encodes an online-constructed dense map, along with object-level semantic and consistency estimates into a control barrier function (CBF) to regulate safe regions in the scene. A model predictive controller (MPC) leverages the CBF-based safety constraints to adapt its navigation behaviour, which is particularly crucial when potential scene changes occur. We test the system in simulations and real-world experiments to demonstrate the impact of semantic information and scene change handling on robot behavior, validating the practicality of our approach.