arXiv subjects

Haobo Liang

Publications and source records attributed to Haobo Liang.

8 recordsLinked to original sources

arXiv2026

Shell-Supervised Gaussian Splatting for Urban Real-to-Sim Reconstruction

Real-to-sim reconstruction for embodied AI requires geometry that is useful for collision reasoning, navigation, and agent-environment interaction, not only photorealistic novel-view synthesis. However, close-range urban facades are difficult for video-to-3D reconstruction: glass, reflections, repeated windows, and weak texture can produce visually plausible renderings with unstable surface geometry. We introduce shell-supervised Gaussian Splatting, a reconstruction-stage framework that uses an external facade structural shell as lightweight geometric supervision for video-driven Gaussian reconstruction. The method aligns an exterior shell to the video reconstruction frame, renders per-view depth, camera-space normal, and valid-mask maps, and applies these cues through mask-gated losses during Gaussian optimization. This design preserves RGB-driven appearance while regularizing only visible shell-supported facade regions. Experiments on anonymized close-range urban facade scenes show improved facade orientation and visible-surface point-cloud consistency over photo-only, monocular-cue, and surface-oriented Gaussian baselines, while maintaining comparable held-out rendering quality.

cs.CV↗

arXiv2026

TacSE3: Equivariant SE(3) Motion Estimation from Low-Texture Visuotactile Images for In-Gripper Tracking and Compensation

Robotic in-hand manipulation requires reliable object-motion tracking under frequent visual occlusion, yet low-texture visuotactile images provide few stable correspondences for conventional image- or geometry-matching methods. This paper presents TacSE3, a tactile motion-estimation pipeline that converts low-texture visuotactile observations into a decoupled three-dimensional force field and estimates incremental rigid-body motion on SE(3). The method derives planar translation from contact-centroid motion and estimates rotation primarily from shear-related tactile responses, yielding a physically interpretable signal for in-gripper tracking and compensation. Experiments with paired DM-Tac fingertip sensors show that dual-sensor sensing reduces translation-rotation ambiguity, supports rotation tracking across axes and object geometries, and provides a lightweight compensation signal that improves disturbance tolerance in downstream manipulation tasks without retraining the base policy.

cs.RO↗

arXiv2026

Provably Safe Trajectory Generation for Manipulators Under Motion and Environmental Uncertainties

Robot manipulators operating in uncertain and non-convex environments present significant challenges for safe and optimal motion planning. Existing methods often struggle to provide efficient and formally certified collision risk guarantees, particularly when dealing with complex geometries and non-Gaussian uncertainties. This article proposes a novel risk-bounded motion planning framework to address this unmet need. Our approach integrates a rigid manipulator deep stochastic Koopman operator (RM-DeSKO) model to robustly predict the robot's state distribution under motion uncertainty. We then introduce an efficient, hierarchical verification method that combines parallelizable physics simulations with sum-of-squares (SOS) programming as a filter for fine-grained, formal certification of collision risk. This method is embedded within a Model Predictive Path Integral (MPPI) controller that uniquely utilizes binary collision information from SOS decomposition to improve its policy. The effectiveness of the proposed framework is validated on two typical robot manipulators through extensive simulations and real-world experiments, including a challenging human-robot collaboration scenario, demonstrating sim-to-real transfer of the learned model and its ability to generate safe and efficient trajectories in complex, uncertain settings.

cs.RO↗

arXiv2025

OpenTie: Open-vocabulary Sequential Rebar Tying System

Robotic practices on the construction site emerge as an attention-attracting manner owing to their capability of tackling complex challenges, especially in the rebar-involved scenarios. Most of existing products and research are mainly focused on the collection of large amounts of data with model training demands. To fulfill this gap, we propose OpenTie, a 3D training-free rebar tying framework utilizing a RGB-to-point-cloud generation and an open-vocabulary rebar detection on the real-world test. We implement the OpenTie via a robotic arm with a binocular camera and guarantee a high accuracy by applying the prompt-based object detection method on the image filtered by our proposed post-processing procedure for the image-to-point-cloud generation framework. Our pipeline requires no training efforts and outperforms the training-based object detection, i.e., YOLO-based method, with the verification on the real-world sequential rebar tying test. The system is flexible for horizontal and vertical rebar tying tasks and holds the potential application to the real construction site with possibility of commercialization.

cs.RO↗

arXiv2025

Learning-based safety lifting monitoring system for cranes on construction sites

Lifting on construction sites, as a frequent operation, works still with safety risks, especially for modular integrated construction (MiC) lifting due to its large weight and size, probably leading to accidents, causing damage to the modules, or more critically, posing safety hazards to on-site workers. Aiming to reduce the safety risks in lifting scenarios, we design an automated safe lifting monitoring algorithm pipeline based on learning-based methods, and deploy it on construction sites. This work is potentially to increase the safety and efficiency of MiC lifting process via automation technologies. A dataset is created consisting of 1007 image-point cloud pairs (37 MiC liftings). Advanced object detection models are trained for automated two-dimensional (2D) detection of MiCs and humans. Fusing the 2D detection results with the point cloud information allows accurate determination of the three-dimensional (3D) positions of MiCs and humans. The system is designed to automatically trigger alarms that notify individuals in the MiC lifting danger zone, while providing the crane operator with real-time lifting information and early warnings. The monitoring process minimizes the human intervention and no or less signal men are required on real sites assisted by our system. A quantitative analysis is conducted to evaluate the effectiveness of the algorithmic pipeline. The pipeline shows promising results in MiC and human perception with the mean distance error of 1.5640 m and 0.7824 m respectively. Furthermore, the developed system successfully executes safety risk monitoring and alarm functionalities during the MiC lifting process with limited manual work on real construction sites.

eess.SY↗

arXiv2025

Bird's-eye view safety monitoring for the construction top under the tower crane

The tower crane is involving more automated and intelligent operation procedure, and importantly, the application of automation technologies to the safety issues is imperative ahead of the utilization of any other advances. Among diverse risk management tasks on site, it is essential to protect the human workers on the workspace between the tower crane and constructed building top area (construction top) from the bird's-eye view, especially with Modular Integrated Construction (MiC) lifted. Also, the camera and Light Detection And Ranging (LiDAR) can capture abundant 3D information on site, which is however yet made the best use. Considering the safety protection for humans and tower cranes, we present an AI-based fully automated safety monitoring system for tower crane lifting from the bird's-eye view, surveilling to shield the human workers on the construction top and avoid cranes' collision by alarming the crane operator. The system achieved a 3D data fusion for localization of humans and MiCs by integrating the captured information from camera and LiDAR. The state-of-the-art methods were explored and implemented into our proposed software pipeline coupled with the hardware and display systems. Furthermore, we conducted an analysis of the components in the pipeline to verify the accuracy and effectiveness of the involved methods. The display and visualization on the real site proved that our system can serve as a valuable safety monitoring toolkit on site.

cs.CV↗

arXiv2025

NTIRE 2025 Image Shadow Removal Challenge Report

This work examines the findings of the NTIRE 2025 Shadow Removal Challenge. A total of 306 participants have registered, with 17 teams successfully submitting their solutions during the final evaluation phase. Following the last two editions, this challenge had two evaluation tracks: one focusing on reconstruction fidelity and the other on visual perception through a user study. Both tracks were evaluated with images from the WSRD+ dataset, simulating interactions between self- and cast-shadows with a large number of diverse objects, textures, and materials.

cs.CV↗

arXiv2025

Quantitative Hardness Assessment with Vision-based Tactile Sensing for Fruit Classification and Grasping

Accurate estimation of fruit hardness is essential for automated classification and handling systems, particularly in determining fruit variety, assessing ripeness, and ensuring proper harvesting force. This study presents an innovative framework for quantitative hardness assessment utilizing vision-based tactile sensing, tailored explicitly for robotic applications in agriculture. The proposed methodology derives normal force estimation from a vision-based tactile sensor, and, based on the dynamics of this normal force, calculates the hardness. This approach offers a rapid, non-destructive evaluation through single-contact interaction. The integration of this framework into robotic systems enhances real-time adaptability of grasping forces, thereby reducing the likelihood of fruit damage. Moreover, the general applicability of this approach, through a universal criterion based on average normal force dynamics, ensures its effectiveness across a wide variety of fruit types and sizes. Extensive experimental validation conducted across different fruit types and ripeness-tracking studies demonstrates the efficacy and robustness of the framework, marking a significant advancement in the domain of automated fruit handling.

cs.RO↗