Vision-Based Adaptive Robotics for Autonomous Surface Crack Repair

• URL: http://arxiv.org/abs/2407.16874v3
• Date: Mon, 11 Aug 2025 23:49:55 GMT
• Title: Vision-Based Adaptive Robotics for Autonomous Surface Crack Repair
• Authors: Joshua Genova, Eric Cabrera, Vedhus Hoskere,
• Abstract summary: This research contributes to the field of human-robot interaction by reducing manual labor, improving safety, and streamlining maintenance operations.<n>We present an adaptive, autonomous robotic system for surface crack detection and repair using advanced sensing technologies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Surface cracks in infrastructure can lead to severe deterioration and expensive maintenance if not efficiently repaired. Manual repair methods are labor-intensive, time-consuming, and imprecise. While advancements in robotic perception and manipulation have progressed autonomous crack repair, three key challenges remain: accurate localization in the robot's coordinate frame, adaptability to varying crack sizes, and realistic validation of repairs. We present an adaptive, autonomous robotic system for surface crack detection and repair using advanced sensing technologies to enhance precision and safety for humans. A laser scanner is used to refine crack coordinates for accurate localization. Furthermore, our adaptive crack filling approach outperforms fixed speed techniques in efficiency and consistency. We validate our method using 3D printed cracks under realistic conditions, demonstrating repeatable testing. This research contributes to the field of human-robot interaction by reducing manual labor, improving safety, and streamlining maintenance operations, ultimately paving the way for more sophisticated and integrated construction robotics.

Related papers

• Efficient Surgical Robotic Instrument Pose Reconstruction in Real World Conditions Using Unified Feature Detection [21.460727996614704]
  MIS robots have long kinematic chains and partial visibility of their degrees of freedom in the camera.<n>We propose a novel framework that unifies the detection of geometric primitives through a shared encoding.<n>This architecture detects both keypoints and edges in a single inference and is trained on large-scale synthetic data with projective labeling.
  arXiv  Detail & Related papers  (2025-10-03T22:03:28Z)
• Software Engineering for Self-Adaptive Robotics: A Research Agenda [19.128553400293008]
  Self-adaptive robotic systems are designed to operate autonomously in dynamic and uncertain environments.<n>Unlike traditional robotic software, self-adaptive robots leverage artificial intelligence, machine learning, and model-driven engineering.<n>This paper presents a research agenda for software engineering in self-adaptive robotics.
  arXiv  Detail & Related papers  (2025-05-26T07:47:50Z)
• Is Single-View Mesh Reconstruction Ready for Robotics? [63.29645501232935]
  This paper evaluates single-view mesh reconstruction models for creating digital twin environments in robot manipulation.<n>We establish benchmarking criteria for 3D reconstruction in robotics contexts.<n>Despite success on computer vision benchmarks, existing approaches fail to meet robotics-specific requirements.
  arXiv  Detail & Related papers  (2025-05-23T14:35:56Z)
• Action Flow Matching for Continual Robot Learning [57.698553219660376]
  Continual learning in robotics seeks systems that can constantly adapt to changing environments and tasks. We introduce a generative framework leveraging flow matching for online robot dynamics model alignment. We find that by transforming the actions themselves rather than exploring with a misaligned model, the robot collects informative data more efficiently.
  arXiv  Detail & Related papers  (2025-04-25T16:26:15Z)
• Prof. Robot: Differentiable Robot Rendering Without Static and Self-Collisions [40.15055686410145]
  We introduce a novel improvement on previous efforts by incorporating physical awareness of collisions through the learning of a neural robotic collision classifier. This enables the optimization of actions that avoid collisions with static, non-interactable environments as well as the robot itself.
  arXiv  Detail & Related papers  (2025-03-14T10:25:54Z)
• Digital Twin-Enabled Real-Time Control in Robotic Additive Manufacturing via Soft Actor-Critic Reinforcement Learning [2.5709786140685633]
  This research presents a novel approach integrating Soft Actor-Critic (SAC) reinforcement learning with digital twin technology. We demonstrate our methodology using a Viper X300s robot arm, implementing two distinct control scenarios. Results show rapid policy convergence and robust task execution in both simulated and physical environments.
  arXiv  Detail & Related papers  (2025-01-29T22:06:53Z)
• Robotic World Model: A Neural Network Simulator for Robust Policy Optimization in Robotics [50.191655141020505]
  This work advances model-based reinforcement learning by addressing the challenges of long-horizon prediction, error accumulation, and sim-to-real transfer. By providing a scalable and robust framework, the introduced methods pave the way for adaptive and efficient robotic systems in real-world applications.
  arXiv  Detail & Related papers  (2025-01-17T10:39:09Z)
• Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Robotics [68.36528819227641]
  This paper systematically evaluates the robustness of Vision-Language-Action (VLA) models.<n>We introduce two untargeted attack objectives that leverage spatial foundations to destabilize robotic actions, and a targeted attack objective that manipulates the robotic trajectory.<n>We design an adversarial patch generation approach that places a small, colorful patch within the camera's view, effectively executing the attack in both digital and physical environments.
  arXiv  Detail & Related papers  (2024-11-18T01:52:20Z)
• Kalib: Markerless Hand-Eye Calibration with Keypoint Tracking [52.4190876409222]
  Hand-eye calibration involves estimating the transformation between the camera and the robot. Recent advancements in deep learning offer markerless techniques, but they present challenges. We propose Kalib, an automatic and universal markerless hand-eye calibration pipeline.
  arXiv  Detail & Related papers  (2024-08-20T06:03:40Z)
• An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems [3.303448701376485]
  Anomaly detection is critical for the secure and reliable operation of industrial control systems. This paper presents a novel deep generative model to meet this need.
  arXiv  Detail & Related papers  (2024-05-03T23:58:27Z)
• Robust Surgical Tool Tracking with Pixel-based Probabilities for Projected Geometric Primitives [28.857732667640068]
  Controlling robotic manipulators via visual feedback requires a known coordinate frame transformation between the robot and the camera. Uncertainties in mechanical systems as well as camera calibration create errors in this coordinate frame transformation. We estimate the camera-to-base transform and joint angle measurement errors for surgical robotic tools using an image based insertion-shaft detection algorithm and probabilistic models.
  arXiv  Detail & Related papers  (2024-03-08T00:57:03Z)
• Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage [12.889670445791985]
  MIDAS is a "deploy-and-forget" approach for automated detection and localization of damage in structures. It is a synergistic integration of entirely passive measurements from inexpensive sensors, data compression, and a mechanics-informed autoencoder. After learning from just 3 hours of data, MIDAS can autonomously detect and localize different types of unforeseen damage.
  arXiv  Detail & Related papers  (2024-02-23T18:31:02Z)
• Self-consistent Validation for Machine Learning Electronic Structure [81.54661501506185]
  Method integrates machine learning with self-consistent field methods to achieve both low validation cost and interpret-ability. This, in turn, enables exploration of the model's ability with active learning and instills confidence in its integration into real-world studies.
  arXiv  Detail & Related papers  (2024-02-15T18:41:35Z)
• Distributional Instance Segmentation: Modeling Uncertainty and High Confidence Predictions with Latent-MaskRCNN [77.0623472106488]
  In this paper, we explore a class of distributional instance segmentation models using latent codes. For robotic picking applications, we propose a confidence mask method to achieve the high precision necessary. We show that our method can significantly reduce critical errors in robotic systems, including our newly released dataset of ambiguous scenes.
  arXiv  Detail & Related papers  (2023-05-03T05:57:29Z)
• EasyHeC: Accurate and Automatic Hand-eye Calibration via Differentiable Rendering and Space Exploration [49.90228618894857]
  We introduce a new approach to hand-eye calibration called EasyHeC, which is markerless, white-box, and delivers superior accuracy and robustness. We propose to use two key technologies: differentiable rendering-based camera pose optimization and consistency-based joint space exploration. Our evaluation demonstrates superior performance in synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-05-02T03:49:54Z)
• A Distance-Geometric Method for Recovering Robot Joint Angles From an RGB Image [7.971699294672282]
  We present a novel method for retrieving the joint angles of a robot manipulator using only a single RGB image of its current configuration. Our approach, based on a distance-geometric representation of the configuration space, exploits the knowledge of a robot's kinematic model.
  arXiv  Detail & Related papers  (2023-01-05T12:57:45Z)
• Revisiting the Adversarial Robustness-Accuracy Tradeoff in Robot Learning [121.9708998627352]
  Recent work has shown that, in practical robot learning applications, the effects of adversarial training do not pose a fair trade-off. This work revisits the robustness-accuracy trade-off in robot learning by analyzing if recent advances in robust training methods and theory can make adversarial training suitable for real-world robot applications.
  arXiv  Detail & Related papers  (2022-04-15T08:12:15Z)
• SERA: Safe and Efficient Reactive Obstacle Avoidance for Collaborative Robotic Planning in Unstructured Environments [1.5229257192293197]
  We propose a novel methodology for reactive whole-body obstacle avoidance. Our approach allows a robotic arm to proactively avoid obstacles of arbitrary 3D shapes without direct contact. Our methodology provides a robust and effective solution for safe human-robot collaboration in non-stationary environments.
  arXiv  Detail & Related papers  (2022-03-24T21:11:43Z)
• Adversarial Training is Not Ready for Robot Learning [55.493354071227174]
  Adversarial training is an effective method to train deep learning models that are resilient to norm-bounded perturbations. We show theoretically and experimentally that neural controllers obtained via adversarial training are subjected to three types of defects. Our results suggest that adversarial training is not yet ready for robot learning.
  arXiv  Detail & Related papers  (2021-03-15T07:51:31Z)
• Learning Compliance Adaptation in Contact-Rich Manipulation [81.40695846555955]
  We propose a novel approach for learning predictive models of force profiles required for contact-rich tasks. The approach combines an anomaly detection based on Bidirectional Gated Recurrent Units (Bi-GRU) and an adaptive force/impedance controller.
  arXiv  Detail & Related papers  (2020-05-01T05:23:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.