# Sight Over Site: Perception-Aware Reinforcement Learning for Efficient Robotic Inspection

arXiv: https://arxiv.org/abs/2509.17877 · HTML: https://mlsanity.com/paper/2509.17877/

## Key facts

- Cited by 1 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 32 of its 45 references that are in the map.
- First posted on arXiv 22 Sep 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, 30 of 32)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [Gibson Env: Real-World Perception for Embodied Agents](https://mlsanity.com/paper/1808.10654/) (2018)
- [Object Goal Navigation using Goal-Oriented Semantic Exploration](https://mlsanity.com/paper/2007.00643/) (2020)
- [DD-PPO: Learning Near-Perfect PointGoal Navigators from 2.5 Billion Frames](https://mlsanity.com/paper/1911.00357/) (2019)
- [ObjectNav Revisited: On Evaluation of Embodied Agents Navigating to Objects](https://mlsanity.com/paper/2006.13171/) (2020)
- [ViNT: A Foundation Model for Visual Navigation](https://mlsanity.com/paper/2306.14846/) (2023)
- [VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation](https://mlsanity.com/paper/2312.03275/) (2023)
- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [GNM: A General Navigation Model to Drive Any Robot](https://mlsanity.com/paper/2210.03370/) (2022)
- [PONI: Potential Functions for ObjectGoal Navigation with Interaction-free Learning](https://mlsanity.com/paper/2201.10029/) (2022)
- [Navigating to Objects in the Real World](https://mlsanity.com/paper/2212.00922/) (2022)
- [Agile But Safe: Learning Collision-Free High-Speed Legged Locomotion](https://mlsanity.com/paper/2401.17583/) (2024)
- [How To Not Train Your Dragon: Training-free Embodied Object Goal Navigation with Semantic Frontiers](https://mlsanity.com/paper/2305.16925/) (2023)
- [NavDP: Learning Sim-to-Real Navigation Diffusion Policy with Privileged Information Guidance](https://mlsanity.com/paper/2505.08712/) (2025)
- [ViPlanner: Visual Semantic Imperative Learning for Local Navigation](https://mlsanity.com/paper/2310.00982/) (2023)
- [An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments](https://mlsanity.com/paper/1909.09548/) (2019)
- [iPlanner: Imperative Path Planning](https://mlsanity.com/paper/2302.11434/) (2023)
- [Is Mapping Necessary for Realistic PointGoal Navigation?](https://mlsanity.com/paper/2206.00997/) (2022)
- [Fast Frontier-based Information-driven Autonomous Exploration with an MAV](https://mlsanity.com/paper/2002.04440/) (2020)
- [Autonomous Power Line Inspection with Drones via Perception-Aware MPC](https://mlsanity.com/paper/2304.00959/) (2023)
- [FrontierNet: Learning Visual Cues to Explore](https://mlsanity.com/paper/2501.04597/) (2025)
- [FAR Planner: Fast, Attemptable Route Planner using Dynamic Visibility Update](https://mlsanity.com/paper/2110.09460/) (2021)
- [SEER: Safe Efficient Exploration for Aerial Robots using Learning to Predict Information Gain](https://mlsanity.com/paper/2209.11034/) (2022)
- [Language-Conditioned Offline RL for Multi-Robot Navigation](https://mlsanity.com/paper/2407.20164/) (2024)
- [ForesightNav: Learning Scene Imagination for Efficient Exploration](https://mlsanity.com/paper/2504.16062/) (2025)
- [Semantic Belief Behavior Graph: Enabling Autonomous Robot Inspection in Unknown Environments](https://mlsanity.com/paper/2401.17191/) (2024)
- [A 3D Mixed Reality Interface for Human-Robot Teaming](https://mlsanity.com/paper/2310.02392/) (2023)
- [Safe Multi-Agent Navigation guided by Goal-Conditioned Safe Reinforcement Learning](https://mlsanity.com/paper/2502.17813/) (2025)

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

- [OpenFrontier: General Navigation with Visual-Language Grounded Frontiers](https://mlsanity.com/paper/2603.05377/) (2026)

## Abstract (arXiv)

Autonomous inspection is a central problem in robotics, with applications ranging from industrial monitoring to search-and-rescue. Traditionally, inspection has often been reduced to navigation tasks, where the objective is to reach a predefined location while avoiding obstacles. However, this formulation captures only part of the real inspection problem. In real-world environments, the inspection targets may become visible well before their exact coordinates are reached, making further movement both redundant and inefficient. What matters more for inspection is not simply arriving at the target's position, but positioning the robot at a viewpoint from which the target becomes observable. In this work, we revisit inspection from a perception-aware perspective. We propose an end-to-end reinforcement learning framework that explicitly incorporates target visibility as the primary objective, enabling the robot to find the shortest trajectory that guarantees visual contact with the target without relying on a map. The learned policy leverages both perceptual and proprioceptive sensing and is trained entirely in simulation, before being deployed to a real-world robot. We further develop an algorithm to compute ground-truth shortest inspection paths, which provides a reference for evaluation. Through extensive experiments, we show that our method outperforms existing classical and learning-based navigation approaches, yielding more efficient inspection trajectories in both simulated and real-world settings. The project is avialable at https://sight-over-site.github.io/