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

2025-09-22

AI Summary

Conventional autonomous inspection simplifies tasks to pose-reaching, ignoring that targets can be observed before reaching their designated poses—leading to redundant navigation paths. Method: This paper proposes a “perception-first” inspection paradigm that optimizes for target visibility as the primary objective, eliminating explicit mapping and predefined pose constraints. We introduce an end-to-end vision-embodied deep reinforcement learning navigation framework. Training is conducted in simulation, with direct deployment in real-world environments; ground-truth visibility-optimal paths are synthesized for quantitative evaluation. Contribution/Results: Experiments demonstrate significant performance gains over classical and state-of-the-art learning-based navigation methods in both simulation and physical settings. To our knowledge, this is the first approach achieving map-free, visibility-driven shortest-path decision-making—substantially improving inspection efficiency and accelerating visual contact establishment.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Vision for Robotics & Autonomous Driving
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Data transparency and provenance
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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/

Problem

• Optimizing robot positioning for target visibility rather than reaching coordinates
• Developing perception-aware inspection without relying on predefined maps
• Finding shortest trajectories that guarantee visual contact with targets

Innovation

• Perception-aware reinforcement learning for inspection
• Target visibility as primary objective function
• Shortest trajectory ensuring visual contact learned