Robotics · Cross-listed from eess.SP
Decoupled Quadratic Kalman Filter for Elliptical Extended Object Tracking with Log-normal Axis Modeling
Simon Steuernagel and Marcus Baum
Extended object tracking involves estimating both the physical extent and kinematic parameters of a target object, where typically multiple measurements are observed per time step. In this article, we propose a deterministic closed-form elliptical extended object tracker, based on decoupling of the kinematics, orientation, and axis lengths. By disregarding potential correlations between these state components, fewer approximations are required for the individual
estimators than for an overall joint solution. This also enables a log-normal representation of the semi-axis lengths, i.e., opposed to related approaches, only positive axis lengths have support. The resulting
algorithm outperforms existing
algorithms, reaching the accuracy of sampling-based procedures. Additionally, a batch-based variant is introduced, yielding highly efficient computation while outperforming all comparable state-of-the-art
algorithms. This is validated both by a simulation study using common models from literature, as well as an extensive quantitative evaluation on real automotive radar data.
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Robotics
Synthetic Data Pipelines for Adaptive, Mission-Ready Militarized Humanoids
Mohammed Ayman Habib and Aldo Petruzzelli
Omnia presents a synthetic data driven pipeline to accelerate the training, validation, and deployment readiness of militarized
humanoids. The approach converts first-person spatial observations captured from point-of-view recordings, smart glasses, augmented reality headsets, and spatial browsing workflows into scalable, mission-specific synthetic
datasets for
humanoid autonomy. By generating large volumes of high-fidelity simulated scenarios and pairing them with automated labeling and model training, the pipeline enables rapid iteration on perception,
navigation, and decision-making capabilities without the cost, risk, or time constraints of extensive field trials. The resulting
datasets can be tuned quickly for new operational environments and threat conditions, supporting both baseline
humanoid performance and advanced subsystems such as
multimodal sensing, counter-detection survivability, and CBRNE-relevant reconnaissance behaviors. This work targets faster development cycles and improved robustness in complex, contested settings by exposing
humanoid systems to broad scenario diversity early in the development process.
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Robotics
A Comprehensive Safety Metric to Evaluate Perception in Autonomous Systems
Georg Volk, Jörg Gamerdinger, Alexander von Bernuth et al. (+1 more)
Complete perception of the environment and its correct interpretation is crucial for autonomous vehicles. Object perception is the main component of automotive surround sensing. Various metrics already exist for the evaluation of object perception. However, objects can be of different importance depending on their velocity, orientation, distance, size, or the potential damage that could be caused by a collision due to a missed detection. Thus, these additional parameters have to be considered for safety evaluation. We propose a new safety metric that incorporates all these parameters and returns a single easily interpretable safety assessment score for object perception. This new metric is evaluated with both real world and virtual data sets and compared to state of the art metrics.
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All authors Georg Volk, Jörg Gamerdinger, Alexander von Bernuth, Oliver Bringmann
Robotics
CoLD Fusion: A Real-time Capable Spline-based Fusion Algorithm for Collective Lane Detection
Jörg Gamerdinger, Sven Teufel, Georg Volk et al. (+1 more)
Comprehensive environment perception is essential for autonomous vehicles to operate safely. It is crucial to detect both dynamic road users and static objects like traffic signs or lanes as these are required for safe motion planning. However, in many circumstances a complete perception of other objects or lanes is not achievable due to limited sensor ranges, occlusions, and curves. In scenarios where an accurate localization is not possible or for roads where no HD maps are available, an autonomous vehicle must rely solely on its perceived road information. Thus, extending local sensing capabilities through collective perception using vehicle-to-vehicle communication is a promising strategy that has not yet been explored for lane detection. Therefore, we propose a real-time capable approach for collective perception of lanes using a spline-based estimation of undetected road sections. We evaluate our proposed fusion
algorithm in various situations and road types. We were able to achieve real-time capability and extend the perception range by up to 200%.
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All authors Jörg Gamerdinger, Sven Teufel, Georg Volk, Oliver Bringmann
Robotics
Fine-Tuning of Neural Network Approximate MPC without Retraining via Bayesian Optimization
Henrik Hose, Paul Brunzema, Alexander von Rohr et al. (+3 more)
Approximate model-predictive control (AMPC) aims to imitate an MPC's behavior with a neural network, removing the need to solve an expensive optimization problem at runtime. However, during deployment, the parameters of the underlying MPC must usually be fine-tuned. This often renders AMPC impractical as it requires repeatedly generating a new
dataset and retraining the neural network. Recent work addresses this problem by adapting AMPC without retraining using approximated sensitivities of the MPC's optimization problem. Currently, this adaption must be done by hand, which is labor-intensive and can be unintuitive for high-dimensional systems. To solve this issue, we propose using
Bayesian optimization to tune the parameters of AMPC policies based on experimental data. By combining model-based control with direct and local learning, our approach achieves superior performance to nominal AMPC on hardware, with minimal experimentation. This allows automatic and data-efficient adaptation of AMPC to new system instances and fine-tuning to cost functions that are difficult to directly implement in MPC. We demonstrate the proposed method in hardware experiments for the swing-up maneuver on an inverted cartpole and yaw control of an under-actuated balancing unicycle
robot, a challenging control problem.
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All authors Henrik Hose, Paul Brunzema, Alexander von Rohr, Alexander Gräfe, Angela P. Schoellig, Sebastian Trimpe
Robotics · Cross-listed from eess.SY
A Geometric Task-Space Port-Hamiltonian Formulation for Redundant Manipulators
Federico Califano, Camilla Rota, Riccardo Zanella et al. (+1 more)
We present a novel
geometric port-
Hamiltonian formulation of redundant manipulators performing a differential kinematic task $\eta=J(q)\dot{q}$, where $q$ is a point on the configuration
manifold, $\eta$ is a velocity-like task space variable, and $J(q)$ is a linear map representing the task, for example the classical analytic or
geometric manipulator Jacobian
matrix. The proposed model emerges from a change of coordinates from canonical
Hamiltonian dynamics, and splits the standard
Hamiltonian momentum variable into a task-space momentum variable and a null-space momentum variable. Properties of this model and relation to Lagrangian formulations present in the literature are highlighted. Finally, we apply the proposed model in an Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) design to stabilize and shape the impedance of a 7-DOF Emika Panda
robot in simulation.
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All authors Federico Califano, Camilla Rota, Riccardo Zanella, Antonio Franchi
Robotics
Field evaluation and optimization of a lightweight autonomous lidar-based UAV system based on a rigorous experimental setup in boreal forest environments
Aleksi Karhunen, Teemu Hakala, Väinö Karjalainen et al. (+1 more)
Interest in utilizing autonomous uncrewed aerial vehicles (
UAVs) for under-canopy forest remote sensing has increased in recent years, resulting in the publication of numerous autonomous flight
algorithms in the scientific literature. To support the selection and development of such
algorithms, a reliable comparison of existing approaches based on published studies is essential. However, reliable comparisons are currently challenging due to widely varying experimental setups and incomplete reporting practices. This study proposes a standardized experimental setup for evaluating autonomous under-canopy
UAV systems to fill this gap. The proposed setup emphasizes quantitative reporting of forest complexity, visual representation of test environments, execution of multiple repeated flights, and reporting of flight success rates alongside qualitative flight results. In addition, flights at multiple target speeds are encouraged, with reporting of realized flight speed, mission completion time, and point-to-point flight distance. The proposed setup is demonstrated using a lightweight
lidar-based quadrotor employing state-of-the-art open-source
algorithms, evaluated through extensive experiments in two natural boreal forest environments. Based on a systematic evaluation of the original system, several improvements were introduced. The same experimental protocol was then repeated with the optimized system, resulting in a total of 93 real-world flights. The optimized system achieved success rates of 12/15 and 15/15 at target flight speeds of 1 m/s and 2 m/s, respectively, in a medium-difficulty forest, and 12/15 and 5/15 in a difficult forest. Adoption of the proposed experimental setup would facilitate the literature-based comparison of autonomous under-canopy flight systems and support systematic performance improvement of future
UAV-based forest robotics solutions.
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All authors Aleksi Karhunen, Teemu Hakala, Väinö Karjalainen, Eija Honkavaara
Robotics
ARCADE: Adaptive Robot Control with Online Changepoint-Aware Bayesian Dynamics Learning
Rishabh Dev Yadav, Avirup Das, Hongyu Song et al. (+2 more)
Real-world
robots must operate under evolving dynamics caused by changing operating conditions, external disturbances, and unmodeled effects. These may appear as gradual drifts, transient fluctuations, or abrupt shifts, demanding real-time adaptation that is robust to short-term variation yet responsive to lasting change. We propose a framework for modeling the nonlinear dynamics of robotic systems that can be updated in real time from streaming data. The method decouples representation learning from online adaptation, using latent representations learned offline to support online closed-form
Bayesian updates. To handle evolving conditions, we introduce a changepoint-aware mechanism with a latent variable inferred from data likelihoods that indicates continuity or shift. When continuity is likely, evidence accumulates to refine predictions; when a shift is detected, past information is tempered to enable rapid re-learning. This maintains calibrated uncertainty and supports probabilistic
reasoning about transient, gradual, or structural change. We prove that the adaptive regret of the framework grows only logarithmically in time and linearly with the number of shifts, competitive with an oracle that knows timings of shift. We validate on cartpole simulations and real quadrotor flights with swinging payloads and mid-flight drops, showing improved predictive accuracy, faster recovery, and more accurate closed-loop tracking than relevant baselines.
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All authors Rishabh Dev Yadav, Avirup Das, Hongyu Song, Samuel Kaski, Wei Pan
Robotics
CaFe-TeleVision: A Coarse-to-Fine Teleoperation System with Immersive Situated Visualization for Enhanced Ergonomics
Zixin Tang, Yiming Chen, Quentin Rouxel et al. (+3 more)
Teleoperation presents a promising paradigm for remote control and
robot proprioceptive data collection. Despite recent progress, current teleoperation systems still suffer from limitations in efficiency and ergonomics, particularly in challenging scenarios. In this paper, we propose CaFe-TeleVision, a coarse-to-fine teleoperation system with immersive situated visualization for enhanced ergonomics. At its core, a coarse-to-fine control mechanism is proposed in the retargeting module to bridge workspace disparities, jointly optimizing efficiency and physical ergonomics. To stream immersive feedback with adequate visual cues for human
vision systems, an on-demand situated visualization technique is integrated in the perception module, which reduces the cognitive load for multi-view processing. The system is built on a
humanoid collaborative
robot and validated with six challenging bimanual manipulation tasks. User study among 24 participants confirms that CaFe-TeleVision enhances ergonomics with statistical significance, indicating a lower task load and a higher user acceptance during teleoperation. Quantitative results also validate the superior performance of our system across six tasks, surpassing comparative methods by up to 28.89% in success rate and accelerating by 26.81% in completion time. Project webpage: https://clover-cuhk.github.io/cafe_television/
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All authors Zixin Tang, Yiming Chen, Quentin Rouxel, Dianxi Li, Shuang Wu, Fei Chen
Robotics · Cross-listed from cs.CV
Xichen Ding, Jianzhe Gao, Cong Pan et al. (+2 more)
Aerial
Vision-and-
Language Navigation (AVLN) requires Unmanned Aerial Vehicle (
UAV)
agents to localize targets in large-scale urban environments based on linguistic instructions. While successful
navigation demands both global environmental
reasoning and local scene comprehension, existing
UAV agents typically adopt mono-granularity frameworks that struggle to balance these two aspects. To address this limitation, this work proposes a History-Enhanced Two-Stage
Transformer (HETT) framework, which integrates the two aspects through a coarse-to-fine
navigation pipeline. Specifically, HETT first predicts coarse-grained target positions by fusing spatial landmarks and historical context, then refines actions via fine-grained visual analysis. In addition, a historical grid map is designed to dynamically aggregate visual features into a structured spatial
memory, enhancing comprehensive scene awareness. Additionally, the CityNav
dataset annotations are manually refined to enhance data quality. Experiments on the refined CityNav
dataset show that HETT delivers significant performance gains, while extensive ablation studies further verify the effectiveness of each component.
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All authors Xichen Ding, Jianzhe Gao, Cong Pan, Wenguan Wang, Jie Qin
Robotics · Cross-listed from cs.CV
DRAW2ACT: Turning Depth-Encoded Trajectories into Robotic Demonstration Videos
Yang Bai, Liudi Yang, George Eskandar et al. (+4 more)
Video diffusion models provide powerful real-world simulators for
embodied AI but remain limited in controllability for robotic manipulation. Recent works on trajectory-conditioned
video generation address this gap but often rely on 2D trajectories or single modality conditioning, which restricts their ability to produce controllable and consistent robotic demonstrations. We present DRAW2ACT, a depth-aware trajectory-conditioned
video generation framework that extracts multiple orthogonal representations from the input trajectory, capturing depth, semantics, shape and motion, and injects them into the
diffusion model. Moreover, we propose to jointly generate spatially aligned RGB and depth
videos, leveraging cross-modality
attention mechanisms and depth supervision to enhance the spatio-temporal consistency. Finally, we introduce a
multimodal policy model conditioned on the generated RGB and depth sequences to regress the
robot's joint angles. Experiments on Bridge V2, Berkeley Autolab, and simulation
benchmarks show that DRAW2ACT achieves superior visual fidelity and consistency while yielding higher manipulation success rates compared to existing baselines.
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All authors Yang Bai, Liudi Yang, George Eskandar, Fengyi Shen, Mohammad Altillawi, Ziyuan Liu, Gitta Kutyniok
Robotics
Trajectory Tracking for Multi-Manipulator Systems in Constrained Environments
Mayank Sewlia, Christos K. Verginis and Dimos V. Dimarogonas
We consider the problem of cooperative manipulation by a mobile multi-manipulator system operating in obstacle-cluttered and highly constrained environments under spatio-temporal task specifications. The task requires transporting a grasped object while respecting both continuous
robot dynamics and discrete
geometric constraints arising from obstacles and narrow passages. To address this hybrid structure, we propose a multi-rate planning and control framework that combines offline generation of an STL-satisfying object trajectory and collision-free base footprints with online constrained inverse kinematics and continuous-time feedback control. The resulting closed-loop system enables coordinated reconfiguration of multiple manipulators while tracking the desired object motion. The approach is evaluated in high-fidelity physics simulations using three Franka Emika Panda mobile manipulators rigidly grasping an object.
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Robotics
SUPER – A Framework for Sensitivity-based Uncertainty-aware Performance and Risk Assessment in Visual Inertial Odometry
Johannes A. Gaus, Daniel Häufle and Woo-Jeong Baek
While many visual odometry (VO), visual-inertial odometry (VIO), and SLAM systems achieve high accuracy, the majority of existing methods miss to assess risks at runtime. This paper presents SUPER (Sensitivity-based Uncertainty-aware PErformance and Risk assessment) that is a generic and explainable framework that propagates uncertainties via sensitivities for real-time risk assessment in VIO. The scientific novelty lies in the derivation of a real-time risk indicator that is backend-agnostic and exploits the Schur complement blocks of the Gauss-Newton normal
matrix to propagate uncertainties. Practically, the Schur complement captures the sensitivity that reflects the influence of the uncertainty on the risk occurrence. Our framework estimates risks on the basis of the residual magnitudes,
geometric conditioning, and short horizon temporal trends without requiring ground truth knowledge. Our framework enables to reliably predict trajectory degradation 50 frames ahead with an improvement of 20% to the baseline. In addition, SUPER initiates a stop or relocalization policy with 89.1% recall. The framework is backend agnostic and operates in real time with less than 0.2% additional CPU cost. Experiments show that SUPER provides consistent uncertainty estimates. A SLAM evaluation highlights the applicability to long horizon mapping.
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Robotics
Interactive Motion Planning for Human-Robot Collaboration Based on Human-Centric Configuration Space Ergonomic Field
Chenzui Li, Yiming Chen, Xi Wu et al. (+4 more)
Industrial human-
robot collaboration requires motion planning that is collision-free, responsive, and ergonomically safe to reduce fatigue and musculoskeletal risk. We propose the Configuration Space Ergonomic Field (CSEF), a continuous and differentiable field over the human joint space that quantifies ergonomic quality and provides gradients for real-time ergonomics-aware planning. An efficient
algorithm constructs CSEF from established metrics with joint-wise weighting and task conditioning, and we integrate it into a gradient-based planner compatible with impedance-controlled
robots. In a 2-DoF
benchmark, CSEF-based planning achieves higher success rates, lower ergonomic cost, and faster computation than a task-space ergonomic planner. Hardware experiments with a dual-arm
robot in unimanual guidance, collaborative drilling, and bimanual cocarrying show faster ergonomic cost reduction, closer tracking to optimized joint targets, and lower muscle activation than a point-to-point baseline. CSEF-based planning method reduces average ergonomic scores by up to 10.31% for collaborative drilling tasks and 5.60% for bimanual co-carrying tasks while decreasing activation in key muscle groups, indicating practical benefits for real-world deployment.
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All authors Chenzui Li, Yiming Chen, Xi Wu, Tao Teng, Sylvain Calinon, Darwin Caldwell, Fei Chen
Robotics
Context Representation via Action-Free Transformer encoder-decoder for Meta Reinforcement Learning
Amir M. Soufi Enayati, Homayoun Honari and Homayoun Najjaran
Reinforcement learning (RL) enables
robots to operate in uncertain environments, but standard approaches often struggle with poor generalization to unseen tasks. Context-adaptive meta reinforcement learning addresses these limitations by conditioning on the task representation, yet they mostly rely on complete action information in the experience making task inference tightly coupled to a specific policy. This paper introduces Context Representation via Action Free
Transformer encoder decoder (CRAFT), a belief model that infers task representations solely from sequences of states and rewards. By removing the dependence on actions, CRAFT decouples task inference from policy optimization, supports modular training, and leverages amortized variational inference for scalable belief updates. Built on a
transformer encoder decoder with rotary positional
embeddings, the model captures long range temporal dependencies and robustly encodes both parametric and non-parametric task variations. Experiments on the MetaWorld ML-10 robotic manipulation
benchmark show that CRAFT achieves faster adaptation, improved generalization, and more effective exploration compared to context adaptive meta–RL baselines. These findings highlight the potential of action-free inference as a foundation for scalable RL in robotic control.
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Robotics
Expert Switching for Robust AAV Landing: A Dual-Detector Framework in Simulation
Humaira Tasnim, Ashik E Rasul, Bruce Jo et al. (+1 more)
Reliable helipad detection is essential for Autonomous Aerial Vehicle (AAV) landing, especially under GPS-denied or visually degraded conditions. While modern detectors such as YOLOv8 offer strong baseline performance, single-model pipelines struggle to remain robust across the extreme scale transitions that occur during descent, where helipads appear small at high altitude and large near touchdown. To address this limitation, we propose a scale-adaptive dual-expert perception framework that decomposes the detection task into far-range and close-range regimes. Two YOLOv8 experts are trained on scale-specialized versions of the HelipadCat
dataset, enabling one model to excel at detecting small, low-resolution helipads and the other to provide high-precision localization when the target dominates the field of view. During inference, both experts operate in parallel, and a
geometric gating mechanism selects the expert whose prediction is most consistent with the AAV's viewpoint. This adaptive routing prevents the degradation commonly observed in single-detector systems when operating across wide altitude ranges. The dual-expert perception module is evaluated in a closed-loop landing environment that integrates CARLA's photorealistic rendering with NASA's GUAM flight-dynamics engine. Results show substantial improvements in
alignment stability, landing accuracy, and overall robustness compared to single-detector baselines. By introducing a scale-aware expert routing strategy tailored to the landing problem, this work advances resilient
vision-based perception for autonomous descent and provides a foundation for future multi-expert AAV frameworks.
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All authors Humaira Tasnim, Ashik E Rasul, Bruce Jo, Hyung-Jin Yoon
Robotics
E-Navi: Environmental Adaptive Navigation for UAVs on Resource Constrained Platforms
Boyang Li, Zhongpeng Jin, Shuai Zhao et al. (+5 more)
The ability to adapt to changing environments is crucial for the autonomous
navigation systems of Unmanned Aerial Vehicles (
UAVs). However, existing
navigation systems adopt fixed execution configurations without considering environmental dynamics based on available computing resources, e.g., with a high execution frequency and task workload. This static approach causes rigid flight strategies and excessive computations, ultimately degrading flight performance or even leading to failures in
UAVs. Despite the necessity for an adaptive system, dynamically adjusting workloads remains challenging, due to difficulties in quantifying environmental complexity and modeling the relationship between environment and system configuration. Aiming at adapting to dynamic environments, this paper proposes E-Navi, an environmental-adaptive
navigation system for
UAVs that dynamically adjusts task executions on the CPUs in response to environmental changes based on available computational resources. Specifically, the perception-planning pipeline of
UAVs navigation system is redesigned through dynamic adaptation of mapping resolution and execution frequency, driven by the quantitative environmental complexity evaluations. In addition, E-Navi supports flexible deployment across hardware platforms with varying levels of computing capability. Extensive Hardware-In-the-Loop and real-world experiments demonstrate that the proposed system significantly outperforms the baseline method across various hardware platforms, achieving up to 53.9%
navigation task workload reduction, up to 63.8% flight time savings, and delivering more stable velocity control.
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All authors Boyang Li, Zhongpeng Jin, Shuai Zhao, Jiahui Liao, Tian Liu, Han Liu, Yuanhai Zhang, Kai Huang
Robotics
Sample-Efficient Robot Skill Learning for Construction Tasks: Benchmarking Hierarchical Reinforcement Learning and Vision-Language-Action VLA Model
Zhaofeng Hu, Hongrui Yu, Vaidhyanathan Chandramouli et al. (+1 more)
This study evaluates two leading approaches for teaching construction
robots new skills to understand their applicability for construction automation: a
Vision-
Language-Action (VLA) model and Reinforcement Learning (RL) methods. The goal is to understand both task performance and the practical effort needed to deploy each approach on real jobs. The authors developed two teleoperation interfaces to control the
robots and collect the demonstrations needed, both of which proved effective for training
robots for long-horizon and dexterous tasks. In addition, the authors conduct a three-stage evaluation. First, the authors compare a Multi-Layer Perceptron (MLP) policy with a Deep Q-network (DQN) imitation model to identify the stronger RL baseline, focusing on model performance, generalization, and a pick-up experiment. Second, three different VLA models are trained in two different scenarios and compared with each other. Third, the authors
benchmark the selected RL baseline against the VLA model using computational and sample-efficiency measures and then a
robot experiment on a multi-stage panel installation task that includes transport and installation. The VLA model demonstrates strong generalization and few-shot capability, achieving 60% and 100% success in the pickup phase. In comparison, DQN can be made robust but needs additional noise during tuning, which increases the workload. Overall, the findings indicate that VLA offers practical advantages for changing tasks by reducing programming effort and enabling useful performance with minimal data, while DQN provides a viable baseline when sufficient tuning effort is acceptable.
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All authors Zhaofeng Hu, Hongrui Yu, Vaidhyanathan Chandramouli, Ci-Jyun Liang
Robotics
CLAIM: Camera-LiDAR Alignment with Intensity and Monodepth
Zhuo Zhang, Yonghui Liu, Meijie Zhang et al. (+2 more)
In this paper, we unleash the potential of the powerful monodepth model in camera-
LiDAR calibration and propose CLAIM, a novel method of aligning data from the camera and
LiDAR. Given the initial guess and pairs of images and
LiDAR point clouds, CLAIM utilizes a coarse-to-fine searching method to find the optimal transformation minimizing a patched Pearson correlation-based structure loss and a mutual information-based texture loss. These two losses serve as good metrics for camera-
LiDAR alignment results and require no complicated steps of data processing, feature extraction, or feature matching like most methods, rendering our method simple and adaptive to most scenes. We validate CLAIM on public KITTI, Waymo, and MIAS-LCEC
datasets, and the experimental results demonstrate its superior performance compared with the state-of-the-art methods. The code is available at https://github.com/Tompson11/claim.
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All authors Zhuo Zhang, Yonghui Liu, Meijie Zhang, Feiyang Tan, Yikang Ding
Robotics
Impact of Robot Facial-Audio Expressions on Human Robot Trust Dynamics and Trust Repair
Hossein Naderi, Alireza Shojaei, Philip Agee et al. (+2 more)
Despite recent advances in robotics and human-
robot collaboration in the AEC industry, trust has mostly been treated as a static factor, with little guidance on how it changes across events during collaboration. This paper investigates how a
robot's task performance and its expressive responses after outcomes shape the dynamics of human trust over time. To this end, we designed a controlled within-subjects study with two construction-inspired tasks, Material Delivery (physical assistance) and Information Gathering (perceptual assistance), and measured trust repeatedly (four times per task) using the 14-item Trust Perception Scale for HRI plus a redelegation choice. The
robot produced two
multimodal expressions, a "glad" display with a brief confirmation after success, and a "sad" display with an apology and a request for a second chance after failure. The study was conducted in a lab environment with 30 participants and a quadruped platform, and we evaluated trust dynamics and repair across both tasks. Results show that
robot success reliably increases trust, failure causes sharp drops, and apology-based expressions partially restores trust (44% recovery in Material Delivery; 38% in Information Gathering). Item-level analysis indicates that recovered trust was driven mostly by interaction and communication factors, with competence recovering partially and autonomy aspects changing least. Additionally, age group and prior attitudes moderated trust dynamics with younger participants showed larger but shorter-lived changes, mid-20s participants exhibited the most durable repair, and older participants showed most conservative dynamics. This work provides a foundation for future efforts that adapt repair strategies to task demands and user profiles to support safe, productive adoption of
robots on construction sites.
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All authors Hossein Naderi, Alireza Shojaei, Philip Agee, Kereshmeh Afsari, Abiola Akanmu
Robotics
Autonomous Construction-Site Safety Inspection Using Mobile Robots: A Multilayer VLM-LLM Pipeline
Hossein Naderi, Alireza Shojaei, Philip Agee et al. (+2 more)
Construction safety inspection remains mostly manual, and automated approaches still rely on task-specific
datasets that are hard to maintain in fast-changing construction environments due to frequent retraining. Meanwhile, field inspection with
robots still depends on human teleoperation and manual reporting, which are labor-intensive. This paper aims to connect what a
robot sees during autonomous
navigation to the safety rules that are common in construction sites, automatically generating a safety inspection report. To this end, we proposed a multi-layer framework with two main modules: robotics and AI. On the robotics side, SLAM and autonomous
navigation provide repeatable coverage and targeted revisits via waypoints. On AI side, a
Vision Language Model (VLM)-based layer produces scene descriptions; a
retrieval component powered grounds those descriptions in OSHA and site policies; Another VLM-based layer assesses the safety situation based on rules; and finally Large
Language Model (LLM) layer generates safety reports based on previous outputs. The framework is validated with a proof-of-concept implementation and evaluated in a lab environment that simulates common hazards across three scenarios. Results show high recall with competitive precision compared to state-of-the-art closed-source models. This paper contributes a transparent, generalizable pipeline that moves beyond black-box models by exposing intermediate artifacts from each layer and keeping the human in the loop. This work provides a foundation for future extensions to additional tasks and settings within and beyond construction context.
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All authors Hossein Naderi, Alireza Shojaei, Philip Agee, Kereshmeh Afsari, Abiola Akanmu