# Semantically-driven Deep Reinforcement Learning for Inspection Path Planning

arXiv: https://arxiv.org/abs/2505.14443 · HTML: https://mlsanity.com/paper/2505.14443/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 22 of its 37 references that are in the map.
- First posted on arXiv 20 May 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 22 of 22)

- [IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures](https://mlsanity.com/paper/1802.01561/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [Learning Exploration Policies for Navigation](https://mlsanity.com/paper/1903.01959/) (2019)
- [Sample Factory: Egocentric 3D Control from Pixels at 100000 FPS with Asynchronous Reinforcement Learning](https://mlsanity.com/paper/2006.11751/) (2020)
- [FUEL: Fast UAV Exploration using Incremental Frontier Structure and Hierarchical Planning](https://mlsanity.com/paper/2010.11561/) (2020)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [A Data-driven Model for Interaction-aware Pedestrian Motion Prediction in Object Cluttered Environments](https://mlsanity.com/paper/1709.08528/) (2017)
- [Learning Active Camera for Multi-Object Navigation](https://mlsanity.com/paper/2210.07505/) (2022)
- [Aerial Gym Simulator: A Framework for Highly Parallelized Simulation of Aerial Robots](https://mlsanity.com/paper/2503.01471/) (2025)
- [Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning](https://mlsanity.com/paper/2203.02381/) (2022)
- [Learning-based Methods for Adaptive Informative Path Planning](https://mlsanity.com/paper/2404.06940/) (2024)
- [Adaptive Informative Path Planning with Multimodal Sensing](https://mlsanity.com/paper/2003.09746/) (2020)
- [Neural Control Barrier Functions for Safe Navigation](https://mlsanity.com/paper/2407.19907/) (2024)
- [Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty](https://mlsanity.com/paper/2105.04758/) (2021)
- [SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning](https://mlsanity.com/paper/2208.08307/) (2022)
- [Semantically-enhanced Deep Collision Prediction for Autonomous Navigation using Aerial Robots](https://mlsanity.com/paper/2307.11522/) (2023)
- [Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based Navigation](https://mlsanity.com/paper/2109.11288/) (2021)
- [Semantics-aware Exploration and Inspection Path Planning](https://mlsanity.com/paper/2303.07236/) (2023)
- [Active Learning for UAV-based Semantic Mapping](https://mlsanity.com/paper/1908.11157/) (2019)
- [TIGRIS: An Informed Sampling-based Algorithm for Informative Path Planning](https://mlsanity.com/paper/2203.12830/) (2022)
- [Autonomous Exploration and General Visual Inspection of Ship Ballast Water Tanks using Aerial Robots](https://mlsanity.com/paper/2311.03838/) (2023)

## Built on by (most cited first, 6 of 6)

- [Reinforcement Learning for Active Perception in Autonomous Navigation](https://mlsanity.com/paper/2602.01266/) (2026)
- [Quantum Grid Path Planning Using Parallel QAOA Circuits Based on Minimum Energy Principle](https://mlsanity.com/paper/2510.07413/) (2025)
- [Aerial Inspection Behaviors via RL-based Quadrotor Control for Under-canopy Forest Environments](https://mlsanity.com/paper/2605.19202/) (2026)
- [Fast Human Attention Prediction for Fixation-guided Active Perception in Autonomous Navigation](https://mlsanity.com/paper/2606.20491/) (2026)
- [TAPAS: Throughput-adaptive Perception for Autonomous Systems](https://mlsanity.com/paper/2607.17317/) (2026)
- [CAVEAT: Recurrent Multimodal Diffusion Planning for Mapless Aerial Exploration](https://mlsanity.com/paper/2609.06623/) (2026)

## Abstract (arXiv)

This paper introduces a novel semantics-aware inspection planning policy derived through deep reinforcement learning. Reflecting the fact that within autonomous informative path planning missions in unknown environments, it is often only a sparse set of objects of interest that need to be inspected, the method contributes an end-to-end policy that simultaneously performs semantic object visual inspection combined with collision-free navigation. Assuming access only to the instantaneous depth map, the associated segmentation image, the ego-centric local occupancy, and the history of past positions in the robot's neighborhood, the method demonstrates robust generalizability and successful crossing of the sim2real gap. Beyond simulations and extensive comparison studies, the approach is verified in experimental evaluations onboard a flying robot deployed in novel environments with previously unseen semantics and overall geometric configurations.