# Perception-Based Temporal Logic Planning in Uncertain Semantic Maps

arXiv: https://arxiv.org/abs/2012.10490 · HTML: https://mlsanity.com/paper/2012.10490/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [Safety Guardrails for LLM-Enabled Robots](https://mlsanity.com/paper/2503.07885/) (2025)
- [Conformal Temporal Logic Planning using Large Language Models](https://mlsanity.com/paper/2309.10092/) (2023)
- [Safe Perception-Based Control under Stochastic Sensor Uncertainty using Conformal Prediction](https://mlsanity.com/paper/2304.00194/) (2023)
- [ConformalNL2LTL: Translating Natural Language Instructions into Temporal Logic Formulas with Conformal Correctness Guarantees](https://mlsanity.com/paper/2504.21022/) (2025)
- [Language-Conditioned Path Planning](https://mlsanity.com/paper/2308.16893/) (2023)
- [Accelerated Reinforcement Learning for Temporal Logic Control Objectives](https://mlsanity.com/paper/2205.04424/) (2022)
- [Sample-Efficient Reinforcement Learning with Temporal Logic Objectives: Leveraging the Task Specification to Guide Exploration](https://mlsanity.com/paper/2410.12136/) (2024)
- [Mission-driven Exploration for Accelerated Deep Reinforcement Learning with Temporal Logic Task Specifications](https://mlsanity.com/paper/2311.17059/) (2023)
- [Federated reinforcement learning for robot motion planning with zero-shot generalization](https://mlsanity.com/paper/2403.13245/) (2024)
- [Probabilistic Planning with Prioritized Preferences over Temporal Logic Objectives](https://mlsanity.com/paper/2304.11641/) (2023)
- [Simultaneous Task Allocation and Planning for Multi-Robots under Hierarchical Temporal Logic Specifications](https://mlsanity.com/paper/2401.04003/) (2024)
- [SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation](https://mlsanity.com/paper/2306.16585/) (2023)

## Abstract (arXiv)

This paper addresses a multi-robot planning problem in environments with partially unknown semantics. The environment is assumed to have known geometric structure (e.g., walls) and to be occupied by static labeled landmarks with uncertain positions and classes. This modeling approach gives rise to an uncertain semantic map generated by semantic SLAM algorithms. Our goal is to design control policies for robots equipped with noisy perception systems so that they can accomplish collaborative tasks captured by global temporal logic specifications. To specify missions that account for environmental and perceptual uncertainty, we employ a fragment of Linear Temporal Logic (LTL), called co-safe LTL, defined over perception-based atomic predicates modeling probabilistic satisfaction requirements. The perception-based LTL planning problem gives rise to an optimal control problem, solved by a novel sampling-based algorithm, that generates open-loop control policies that are updated online to adapt to a continuously learned semantic map. We provide extensive experiments to demonstrate the efficiency of the proposed planning architecture.