arXiv subjects

Michele Grimaldi

Publications and source records attributed to Michele Grimaldi.

17 recordsLinked to original sources

arXiv2026

Beyond Shallow-Water Photorealism: Physically and Sensor-Grounded Simulation for Deep-Sea Robotics

Many recent underwater simulators emphasize visual realism at the expense of physical fidelity, focusing on shallow-water effects with limited relevance in deep-water environments and high computational cost. In this work, we shift the focus toward deep-sea physical and sensor realism. We present a physics- and sensor-grounded extension of the Stonefish simulator that augments its hydrodynamic models with stochastic IMU and DVL drift, magnetometer disturbances, higher-order hydrodynamics, terramechanics, pressure-driven environmental variability, and physically based underwater optics. These additions are designed to better capture the forces and measurements shaping the behavior of deep-ocean AUVs, ROVs, landers, ASVs, and gliders, while remaining compatible with real-time simulation. This work advances underwater simulation toward more representative deep-sea operating conditions, which is particularly relevant for long-duration navigation and learning-based autonomy, where inaccurate sensor and environmental models introduce non-physical artifacts and overly optimistic performance. While challenges remain, including complex fluid-structure interactions and full environmental stochasticity, the proposed framework provides a practical foundation for navigation, perception, and autonomy research under deep-sea conditions.

cs.RO↗

arXiv2026

Contact-Aided Factor-Graph Localization for Underwater Sampling

Accurate state estimation for autonomous underwater vehicles performing close-range seafloor sampling remains challenging. In low-altitude operation, down-looking cameras over featureless planar seabeds produce scale ambiguity, lateral degeneracy, and inconsistent feature tracking. Meanwhile, inertial-Doppler Velocity Log (DVL) fusion alone provides no mechanism for structural drift correction. We propose a Contact-Aided Factor-Graph Localization framework that treats physical interaction as an informative geometric constraint within a smoothing-based localization formulation. The method tightly fuses suction-based manipulator contact events with adaptive visual odometry, learned object detections, and on-board sensors. Visual odometry relative-pose factors and landmark bearing-range factors are uncertainty-scaled according to inlier statistics to prevent visually weak frames from destabilizing the estimator, while contact events are modeled as high-confidence factors that induce implicit loop closures without appearance-based place recognition. Furthermore, the system can fully initialize online during motion. Experimental evaluation in tanks, harbor, and simulation environments demonstrates that contact-induced constraints significantly reduce trajectory drift and improve object revisit accuracy compared to filtering-based navigation and contact-free graph formulations. These results highlight the role of embodied physical interaction as a localization primitive in perception-degraded underwater environments

cs.RO↗

arXiv2026

BALTIC: A Benchmark and Cross-Domain Strategy for 3D Reconstruction Across Air and Underwater Domains Under Varying Illumination

Robust 3D reconstruction across varying environmental conditions remains a critical challenge for robotic perception, particularly when transitioning between air and water. To address this, we introduce BALTIC, a controlled benchmark designed to systematically evaluate modern 3D reconstruction methods under variations in medium and lighting. The benchmark comprises 13 datasets spanning two media (air and water) and three lighting conditions (ambient, artificial, and mixed), with additional variations in motion type, scanning pattern, and initialization trajectory, resulting in a diverse set of sequences. Our experimental setup features a custom water tank equipped with a monocular camera and an HTC Vive tracker, enabling accurate ground-truth pose estimation. We further investigate cross-domain reconstruction by augmenting underwater image sequences with a small number of in-air views captured under similar lighting conditions. We evaluate Structure-from-Motion reconstruction using COLMAP in terms of both trajectory accuracy and scene geometry, and use these reconstructions as input to Neural Radiance Fields and 3D Gaussian Splatting methods. The resulting models are assessed against ground-truth trajectories and in-air references, while rendered outputs are compared using perceptual and photometric metrics. Additionally, we perform a color restoration analysis to evaluate radiometric consistency across domains. Our results show that under controlled, texture-consistent conditions, Gaussian Splatting with simple preprocessing (e.g., white balance correction) can achieve performance comparable to specialized underwater methods, although its robustness decreases in more complex and heterogeneous real-world environments

cs.CV↗

arXiv2026

InsSo3D: Inertial Navigation System and 3D Sonar SLAM for turbid environment inspection

This paper presents InsSo3D, an accurate and efficient method for large-scale 3D Simultaneous Localisation and Mapping (SLAM) using a 3D Sonar and an Inertial Navigation System (INS). Unlike traditional sonar, which produces 2D images containing range and azimuth information but lacks elevation information, 3D Sonar produces a 3D point cloud, which therefore does not suffer from elevation ambiguity. We introduce a robust and modern SLAM framework adapted to the 3D Sonar data using INS as prior, detecting loop closure and performing pose graph optimisation. We evaluated InsSo3D performance inside a test tank with access to ground truth data and in an outdoor flooded quarry. Comparisons to reference trajectories and maps obtained from an underwater motion tracking system and visual Structure From Motion (SFM) demonstrate that InsSo3D efficiently corrects odometry drift. The average trajectory error is below 21cm during a 50-minute-long mission, producing a map of 10m by 20m with a 9cm average reconstruction error, enabling safe inspection of natural or artificial underwater structures even in murky water conditions.

cs.RO↗

arXiv2026

eStonefish-Scenes: A Sim-to-Real Validated and Robot-Centric Event-based Optical Flow Dataset for Underwater Vehicles

Event-based cameras (EBCs) are poised to transform underwater robotics, yet the absence of labelled event-based datasets for underwater environments severely limits progress in tasks such as visual odometry and obstacle avoidance. Real-world event-based optical flow datasets are scarce, resource-intensive to collect, and lack diversity, while no prior benchmarks target underwater applications. To bridge this gap, we introduce eStonefish-Scenes, a synthetic event-based optical flow dataset generated using the Stonefish simulator, together with an open data generation pipeline for creating customizable underwater environments featuring realistic coral reefs and biologically inspired schools of fish with reactive navigation behaviours. We also present eWiz, a comprehensive library for event-based data processing, encompassing data loading, augmentation, visualization, encoding, training utilities, loss functions, and evaluation metrics. To validate sim-to-real transferability, we collected real-world data using a DAVIS346 hybrid event-and-frame camera mounted on a BlueROV2 in an indoor testing pool. Ground-truth optical flow was derived via homography-based frame-to-poster registration, and per-pixel uncertainty was estimated through Monte Carlo perturbation of keypoint correspondences. This uncertainty was incorporated into the evaluation metrics, enabling reliability-aware performance assessment. A ConvGRU-based optical flow network, trained exclusively on synthetic eStonefish-Scenes data, was evaluated on the real-world sequences without fine-tuning, achieving an uncertainty-weighted average endpoint error of 0.79 pixels. These results demonstrate that the proposed synthetic dataset effectively supports sim-to-real transfer for underwater event-based optical flow estimation, substantially reducing the need for costly real-world data collection.

cs.CV↗

arXiv2026

Towards reliable subsea object recovery: a simulation study of an auv with a suction-actuated end effector

Autonomous object recovery in the hadal zone is challenging due to extreme hydrostatic pressure, limited visibility and currents, and the need for precise manipulation at full ocean depth. Field experimentation in such environments is costly, high-risk, and constrained by limited vehicle availability, making early validation of autonomous behaviors difficult. This paper presents a simulation-based study of a complete autonomous subsea object recovery mission using a Hadal Small Vehicle (HSV) equipped with a three-degree-of-freedom robotic arm and a suction-actuated end effector. The Stonefish simulator is used to model realistic vehicle dynamics, hydrodynamic disturbances, sensing, and interaction with a target object under hadal-like conditions. The control framework combines a world-frame PID controller for vehicle navigation and stabilization with an inverse-kinematics-based manipulator controller augmented by acceleration feed-forward, enabling coordinated vehicle - manipulator operation. In simulation, the HSV autonomously descends from the sea surface to 6,000 m, performs structured seafloor coverage, detects a target object, and executes a suction-based recovery. The results demonstrate that high-fidelity simulation provides an effective and low-risk means of evaluating autonomous deep-sea intervention behaviors prior to field deployment.

cs.RO↗

arXiv2025

Distributed AI Agents for Cognitive Underwater Robot Autonomy

Achieving robust cognitive autonomy in robots navigating complex, unpredictable environments remains a fundamental challenge in robotics. This paper presents Underwater Robot Self-Organizing Autonomy (UROSA), a groundbreaking architecture leveraging distributed Large Language Model AI agents integrated within the Robot Operating System 2 (ROS 2) framework to enable advanced cognitive capabilities in Autonomous Underwater Vehicles. UROSA decentralises cognition into specialised AI agents responsible for multimodal perception, adaptive reasoning, dynamic mission planning, and real-time decision-making. Central innovations include flexible agents dynamically adapting their roles, retrieval-augmented generation utilising vector databases for efficient knowledge management, reinforcement learning-driven behavioural optimisation, and autonomous on-the-fly ROS 2 node generation for runtime functional extensibility. Extensive empirical validation demonstrates UROSA's promising adaptability and reliability through realistic underwater missions in simulation and real-world deployments, showing significant advantages over traditional rule-based architectures in handling unforeseen scenarios, environmental uncertainties, and novel mission objectives. This work not only advances underwater autonomy but also establishes a scalable, safe, and versatile cognitive robotics framework capable of generalising to a diverse array of real-world applications.

cs.RO↗

arXiv2025

Tethered Multi-Robot Systems in Marine Environments

This paper introduces a novel simulation framework for evaluating motion control in tethered multi-robot systems within dynamic marine environments. Specifically, it focuses on the coordinated operation of an Autonomous Underwater Vehicle (AUV) and an Autonomous Surface Vehicle(ASV). The framework leverages GazeboSim, enhanced with realistic marine environment plugins and ArduPilots SoftwareIn-The-Loop (SITL) mode, to provide a high-fidelity simulation platform. A detailed tether model, combining catenary equations and physical simulation, is integrated to accurately represent the dynamic interactions between the vehicles and the environment. This setup facilitates the development and testing of advanced control strategies under realistic conditions, demonstrating the frameworks capability to analyze complex tether interactions and their impact on system performance.

cs.RO↗

arXiv2025

Framework for Robust Motion Planning of Tethered Multi-Robot Systems in Marine Environments

This paper introduces CoralGuide, a novel framework designed for path planning and trajectory optimization for tethered multi-robot systems. We focus on marine robotics, which commonly have tethered configurations of an Autonomous Surface Vehicle (ASV) and an Autonomous Underwater Vehicle (AUV). CoralGuide provides safe navigation in marine environments by enhancing the A* algorithm with specialized heuristics tailored for tethered ASV-AUV systems. Our method integrates catenary curve modelling for tether management and employs Bezier curve interpolation for smoother trajectory planning, ensuring efficient and synchronized operations without compromising safety. Through simulations and real-world experiments, we have validated CoralGuides effectiveness in improving path planning and trajectory optimization, demonstrating its potential to significantly enhance operational capabilities in marine research and infrastructure inspection.

cs.RO↗

arXiv2025

Advancing Shared and Multi-Agent Autonomy in Underwater Missions: Integrating Knowledge Graphs and Retrieval-Augmented Generation

Robotic platforms have become essential for marine operations by providing regular and continuous access to offshore assets, such as underwater infrastructure inspection, environmental monitoring, and resource exploration. However, the complex and dynamic nature of underwater environments, characterized by limited visibility, unpredictable currents, and communication constraints, presents significant challenges that demand advanced autonomy while ensuring operator trust and oversight. Central to addressing these challenges are knowledge representation and reasoning techniques, particularly knowledge graphs and retrieval-augmented generation (RAG) systems, that enable robots to efficiently structure, retrieve, and interpret complex environmental data. These capabilities empower robotic agents to reason, adapt, and respond effectively to changing conditions. The primary goal of this work is to demonstrate both multi-agent autonomy and shared autonomy, where multiple robotic agents operate independently while remaining connected to a human supervisor. We show how a RAG-powered large language model, augmented with knowledge graph data and domain taxonomy, enables autonomous multi-agent decision-making and facilitates seamless human-robot interaction, resulting in 100\% mission validation and behavior completeness. Finally, ablation studies reveal that without structured knowledge from the graph and/or taxonomy, the LLM is prone to hallucinations, which can compromise decision quality.

cs.RO↗

arXiv2025

Investigation of the Challenges of Underwater-Visual-Monocular-SLAM

In this paper, we present a comprehensive investigation of the challenges of Monocular Visual Simultaneous Localization and Mapping (vSLAM) methods for underwater robots. While significant progress has been made in state estimation methods that utilize visual data in the past decade, most evaluations have been limited to controlled indoor and urban environments, where impressive performance was demonstrated. However, these techniques have not been extensively tested in extremely challenging conditions, such as underwater scenarios where factors such as water and light conditions, robot path, and depth can greatly impact algorithm performance. Hence, our evaluation is conducted in real-world AUV scenarios as well as laboratory settings which provide precise external reference. A focus is laid on understanding the impact of environmental conditions, such as optical properties of the water and illumination scenarios, on the performance of monocular vSLAM methods. To this end, we first show that all methods perform very well in in-air settings and subsequently show the degradation of their performance in challenging underwater environments. The final goal of this study is to identify techniques that can improve accuracy and robustness of SLAM methods in such conditions. To achieve this goal, we investigate the potential of image enhancement techniques to improve the quality of input images used by the SLAM methods, specifically in low visibility and extreme lighting scenarios in scattering media. We present a first evaluation on calibration maneuvers and simple image restoration techniques to determine their ability to enable or enhance the performance of monocular SLAM methods in underwater environments.

cs.RO↗

arXiv2025

Stonefish: Supporting Machine Learning Research in Marine Robotics

Simulations are highly valuable in marine robotics, offering a cost-effective and controlled environment for testing in the challenging conditions of underwater and surface operations. Given the high costs and logistical difficulties of real-world trials, simulators capable of capturing the operational conditions of subsea environments have become key in developing and refining algorithms for remotely-operated and autonomous underwater vehicles. This paper highlights recent enhancements to the Stonefish simulator, an advanced open-source platform supporting development and testing of marine robotics solutions. Key updates include a suite of additional sensors, such as an event-based camera, a thermal camera, and an optical flow camera, as well as, visual light communication, support for tethered operations, improved thruster modelling, more flexible hydrodynamics, and enhanced sonar accuracy. These developments and an automated annotation tool significantly bolster Stonefish's role in marine robotics research, especially in the field of machine learning, where training data with a known ground truth is hard or impossible to collect.

cs.RO↗

arXiv2025

Context-Aware Behavior Learning with Heuristic Motion Memory for Underwater Manipulation

Autonomous motion planning is critical for efficient and safe underwater manipulation in dynamic marine environments. Current motion planning methods often fail to effectively utilize prior motion experiences and adapt to real-time uncertainties inherent in underwater settings. In this paper, we introduce an Adaptive Heuristic Motion Planner framework that integrates a Heuristic Motion Space (HMS) with Bayesian Networks to enhance motion planning for autonomous underwater manipulation. Our approach employs the Probabilistic Roadmap (PRM) algorithm within HMS to optimize paths by minimizing a composite cost function that accounts for distance, uncertainty, energy consumption, and execution time. By leveraging HMS, our framework significantly reduces the search space, thereby boosting computational performance and enabling real-time planning capabilities. Bayesian Networks are utilized to dynamically update uncertainty estimates based on real-time sensor data and environmental conditions, thereby refining the joint probability of path success. Through extensive simulations and real-world test scenarios, we showcase the advantages of our method in terms of enhanced performance and robustness. This probabilistic approach significantly advances the capability of autonomous underwater robots, ensuring optimized motion planning in the face of dynamic marine challenges.

cs.RO↗

arXiv2025

Real-time Seafloor Segmentation and Mapping

Posidonia oceanica meadows are a species of seagrass highly dependent on rocks for their survival and conservation. In recent years, there has been a concerning global decline in this species, emphasizing the critical need for efficient monitoring and assessment tools. While deep learning-based semantic segmentation and visual automated monitoring systems have shown promise in a variety of applications, their performance in underwater environments remains challenging due to complex water conditions and limited datasets. This paper introduces a framework that combines machine learning and computer vision techniques to enable an autonomous underwater vehicle (AUV) to inspect the boundaries of Posidonia oceanica meadows autonomously. The framework incorporates an image segmentation module using an existing Mask R-CNN model and a strategy for Posidonia oceanica meadow boundary tracking. Furthermore, a new class dedicated to rocks is introduced to enhance the existing model, aiming to contribute to a comprehensive monitoring approach and provide a deeper understanding of the intricate interactions between the meadow and its surrounding environment. The image segmentation model is validated using real underwater images, while the overall inspection framework is evaluated in a realistic simulation environment, replicating actual monitoring scenarios with real underwater images. The results demonstrate that the proposed framework enables the AUV to autonomously accomplish the main tasks of underwater inspection and segmentation of rocks. Consequently, this work holds significant potential for the conservation and protection of marine environments, providing valuable insights into the status of Posidonia oceanica meadows and supporting targeted preservation efforts

cs.CV↗

arXiv2025

GRETA: Modular Platform to Create Adaptive Socially Interactive Agents

The interaction between humans is very complex to describe since it is composed of different elements from different modalities such as speech, gaze, and gestures influenced by social attitudes and emotions. Furthermore, the interaction can be affected by some features which refer to the interlocutor's state. Actual Socially Interactive Agents SIAs aim to adapt themselves to the state of the interaction partner. In this paper, we discuss this adaptation by describing the architecture of the GRETA platform which considers external features while interacting with humans and/or another ECA and process the dialogue incrementally. We illustrate the new architecture of GRETA which deals with the external features, the adaptation, and the incremental approach for the dialogue processing.

cs.HC↗

arXiv2024

Integrating a Digital Twin Concept in the Zero Emission Sea Transporter (ZEST) Project for Sustainable Maritime Transport using Stonefish Simulator

In response to stringent emission reduction targets imposed by the International Maritime Organization (IMO) and the European Green Deal's Fit for 55 legislation package, the maritime industry has shifted its focus towards decarbonization. While significant attention has been placed on vessels exceeding 5,000 gross tons (GT), emissions from coastal and short sea shipping, amounting to approximately 13% of global shipping transportation and 15% within the European Union (EU), have not received adequate consideration. This abstract introduces the Zero Emission Sea Transporter (ZEST) project, designed to address this issue by developing a zero-emissions multi-purpose catamaran for short sea route

cs.RO↗

arXiv2022

Zero-Shot Style Transfer for Gesture Animation driven by Text and Speech using Adversarial Disentanglement of Multimodal Style Encoding

Modeling virtual agents with behavior style is one factor for personalizing human agent interaction. We propose an efficient yet effective machine learning approach to synthesize gestures driven by prosodic features and text in the style of different speakers including those unseen during training. Our model performs zero shot multimodal style transfer driven by multimodal data from the PATS database containing videos of various speakers. We view style as being pervasive while speaking, it colors the communicative behaviors expressivity while speech content is carried by multimodal signals and text. This disentanglement scheme of content and style allows us to directly infer the style embedding even of speaker whose data are not part of the training phase, without requiring any further training or fine tuning. The first goal of our model is to generate the gestures of a source speaker based on the content of two audio and text modalities. The second goal is to condition the source speaker predicted gestures on the multimodal behavior style embedding of a target speaker. The third goal is to allow zero shot style transfer of speakers unseen during training without retraining the model. Our system consists of: (1) a speaker style encoder network that learns to generate a fixed dimensional speaker embedding style from a target speaker multimodal data and (2) a sequence to sequence synthesis network that synthesizes gestures based on the content of the input modalities of a source speaker and conditioned on the speaker style embedding. We evaluate that our model can synthesize gestures of a source speaker and transfer the knowledge of target speaker style variability to the gesture generation task in a zero shot setup. We convert the 2D gestures to 3D poses and produce 3D animations. We conduct objective and subjective evaluations to validate our approach and compare it with a baseline.

cs.SD↗