arXiv subjects

Darwin G. Caldwell

Publications and source records attributed to Darwin G. Caldwell.

At least 19 recordsLinked to original sources

arXiv2025

GARField: Addressing the visual Sim-to-Real gap in garment manipulation with mesh-attached radiance fields

While humans intuitively manipulate garments and other textile items swiftly and accurately, it is a significant challenge for robots. A factor crucial to human performance is the ability to imagine, a priori, the intended result of the manipulation intents and hence develop predictions on the garment pose. That ability allows us to plan from highly obstructed states, adapt our plans as we collect more information and react swiftly to unforeseen circumstances. Conversely, robots struggle to establish such intuitions and form tight links between plans and observations. We can partly attribute this to the high cost of obtaining densely labelled data for textile manipulation, both in quality and quantity. The problem of data collection is a long-standing issue in data-based approaches to garment manipulation. As of today, generating high-quality and labelled garment manipulation data is mainly attempted through advanced data capture procedures that create simplified state estimations from real-world observations. However, this work proposes a novel approach to the problem by generating real-world observations from object states. To achieve this, we present GARField (Garment Attached Radiance Field), the first differentiable rendering architecture, to our knowledge, for data generation from simulated states stored as triangle meshes. Code is available on https://ddonatien.github.io/garfield-website/

cs.RO↗

arXiv2025

From Fields to Splats: A Cross-Domain Survey of Real-Time Neural Scene Representations

Neural scene representations such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have transformed how 3D environments are modeled, rendered, and interpreted. NeRF introduced view-consistent photorealism via volumetric rendering; 3DGS has rapidly emerged as an explicit, efficient alternative that supports high-quality rendering, faster optimization, and integration into hybrid pipelines for enhanced photorealism and task-driven scene understanding. This survey examines how 3DGS is being adopted across SLAM, telepresence and teleoperation, robotic manipulation, and 3D content generation. Despite their differences, these domains share common goals: photorealistic rendering, meaningful 3D structure, and accurate downstream tasks. We organize the review around unified research questions that explain why 3DGS is increasingly displacing NeRF-based approaches: What technical advantages drive its adoption? How does it adapt to different input modalities and domain-specific constraints? What limitations remain? By systematically comparing domain-specific pipelines, we show that 3DGS balances photorealism, geometric fidelity, and computational efficiency. The survey offers a roadmap for leveraging neural rendering not only for image synthesis but also for perception, interaction, and content creation across real and virtual environments.

cs.CV↗

arXiv2025

Impact of a Lower Limb Exosuit Anchor Points on Energetics and Biomechanics

Anchor point placement is a crucial yet often overlooked aspect of exosuit design since it determines how forces interact with the human body. This work analyzes the impact of different anchor point positions on gait kinematics, muscular activation and energetic consumption. A total of six experiments were conducted with 11 subjects wearing the XoSoft exosuit, which assists hip flexion in five configurations. Subjects were instrumented with an IMU-based motion tracking system, EMG sensors, and a mask to measure metabolic consumption. The results show that positioning the knee anchor point on the posterior side while keeping the hip anchor on the anterior part can reduce muscle activation in the hip flexors by up to 10.21\% and metabolic expenditure by up to 18.45\%. Even if the only assisted joint was the hip, all the configurations introduced changes also in the knee and ankle kinematics. Overall, no single configuration was optimal across all subjects, suggesting that a personalized approach is necessary to transmit the assistance forces optimally. These findings emphasize that anchor point position does indeed have a significant impact on exoskeleton effectiveness and efficiency. However, these optimal positions are subject-specific to the exosuit design, and there is a strong need for future work to tailor musculoskeletal models to individual characteristics and validate these results in clinical populations.

physics.med-ph↗

arXiv2025

Enhancing Human-Robot Collaboration: A Sim2Real Domain Adaptation Algorithm for Point Cloud Segmentation in Industrial Environments

The robust interpretation of 3D environments is crucial for human-robot collaboration (HRC) applications, where safety and operational efficiency are paramount. Semantic segmentation plays a key role in this context by enabling a precise and detailed understanding of the environment. Considering the intense data hunger for real-world industrial annotated data essential for effective semantic segmentation, this paper introduces a pioneering approach in the Sim2Real domain adaptation for semantic segmentation of 3D point cloud data, specifically tailored for HRC. Our focus is on developing a network that robustly transitions from simulated environments to real-world applications, thereby enhancing its practical utility and impact on a safe HRC. In this work, we propose a dual-stream network architecture (FUSION) combining Dynamic Graph Convolutional Neural Networks (DGCNN) and Convolutional Neural Networks (CNN) augmented with residual layers as a Sim2Real domain adaptation algorithm for an industrial environment. The proposed model was evaluated on real-world HRC setups and simulation industrial point clouds, it showed increased state-of-the-art performance, achieving a segmentation accuracy of 97.76%, and superior robustness compared to existing methods.

cs.RO↗

arXiv2025

Real-time Fall Prevention system for the Next-generation of Workers

Developing a general-purpose wearable real-time fall-detection system is still a challenging task, especially for healthy and strong subjects, such as industrial workers that work in harsh environments. In this work, we present a hybrid approach for fall detection and prevention, which uses the dynamic model of an inverted pendulum to generate simulations of falling that are then fed to a deep learning framework. The output is a signal to activate a fall mitigation mechanism when the subject is at risk of harm. The advantage of this approach is that abstracted models can be used to efficiently generate training data for thousands of different subjects with different falling initial conditions, something that is practically impossible with real experiments. This approach is suitable for a specific type of fall, where the subjects fall without changing their initial configuration significantly, and it is the first step toward a general-purpose wearable device, with the aim of reducing fall-associated injuries in industrial environments, which can improve the safety of workers.

eess.SP↗

arXiv2025

How can AI reduce fall injuries in the workplace?

Fall-caused injuries are common in all types of work environments, including offices. They are the main cause of absences longer than three days, especially for small and medium-sized businesses (SMEs). However, data, data amount, data heterogeneity, and stringent processing time constraints continue to pose challenges to real-time fall detection. This work proposes a new approach based on a recurrent neural network (RNN) for Fall Detection and a Kolmogorov-Arnold Network (KAN) to estimate the time of impact of the fall. The approach is tested on SisFall, a dataset consisting of 2706 Activities of Daily Living (ADLs) and 1798 falls recorded by three sensors. The results show that the proposed approach achieves an average TPR of 82.6% and TNR of 98.4% for fall sequences and 94.4% in ADL. Besides, the Root Mean Squared Error of the estimated time of impact is approximately 160ms.

eess.SP↗

arXiv2025

How can AI reduce wrist injuries in the workplace?

This paper explores the development of a control and sensor strategy for an industrial wearable wrist exoskeleton by classifying and predicting workers' actions. The study evaluates the correlation between exerted force and effort intensity, along with sensor strategy optimization, for designing purposes. Using data from six healthy subjects in a manufacturing plant, this paper presents EMG-based models for wrist motion classification and force prediction. Wrist motion recognition is achieved through a pattern recognition algorithm developed with surface EMG data from an 8-channel EMG sensor (Myo Armband); while a force regression model uses wrist and hand force measurements from a commercial handheld dynamometer (Vernier GoDirect Hand Dynamometer). This control strategy forms the foundation for a streamlined exoskeleton architecture designed for industrial applications, focusing on simplicity, reduced costs, and minimal sensor use while ensuring reliable and effective assistance.

eess.SP↗

arXiv2025

Ergonomic Assessment of Work Activities for an Industrial-oriented Wrist Exoskeleton

Musculoskeletal disorders (MSD) are the most common cause of work-related injuries and lost production involving approximately 1.7 billion people worldwide and mainly affect low back (more than 50%) and upper limbs (more than 40%). It has a profound effect on both the workers affected and the company. This paper provides an ergonomic assessment of different work activities in a horse saddle-making company, involving 5 workers. This aim guides the design of a wrist exoskeleton to reduce the risk of musculoskeletal diseases wherever it is impossible to automate the production process. This evaluation is done either through subjective and objective measurement, respectively using questionnaires and by measurement of muscle activation with sEMG sensors.

eess.SP↗

arXiv2025

Recognition of Physiological Patterns during Activities of Daily Living Using Wearable Biosignal Sensors

A key aspect of developing fall prevention systems is the early prediction of a fall before it occurs. This paper presents a statistical overview of results obtained by analyzing 22 activities of daily living to recognize physiological patterns and estimate the risk of an imminent fall. The results demonstrate distinctive patterns between high-intensity and low-intensity activity using EMG, ECG, and respiration sensors, also indicating the presence of a proportional trend between movement velocity and muscle activity. These outcomes highlight the potential benefits of using these sensors in the future to direct the development of an activity recognition and risk prediction framework for physiological phenomena that can cause fall injuries.

eess.SP↗

arXiv2023

Reactive Landing Controller for Quadruped Robots

Quadruped robots are machines intended for challenging and harsh environments. Despite the progress in locomotion strategy, safely recovering from unexpected falls or planned drops is still an open problem. It is further made more difficult when high horizontal velocities are involved. In this work, we propose an optimization-based reactive Landing Controller that uses only proprioceptive measures for torque-controlled quadruped robots that free-fall on a flat horizontal ground, knowing neither the distance to the landing surface nor the flight time. Based on an estimate of the Center of Mass horizontal velocity, the method uses the Variable Height Springy Inverted Pendulum model for continuously recomputing the feet position while the robot is falling. In this way, the quadruped is ready to attain a successful landing in all directions, even in the presence of significant horizontal velocities. The method is demonstrated to dramatically enlarge the region of horizontal velocities that can be dealt with by a naive approach that keeps the feet still during the airborne stage. To the best of our knowledge, this is the first time that a quadruped robot can successfully recover from falls with horizontal velocities up to 3 m/s in simulation. Experiments prove that the used platform, Go1, can successfully attain a stable standing configuration from falls with various horizontal velocity and different angular perturbations.

cs.RO↗

arXiv2023

Kinematically-Decoupled Impedance Control for Fast Object Visual Servoing and Grasping on Quadruped Manipulators

We propose a control pipeline for SAG (Searching, Approaching, and Grasping) of objects, based on a decoupled arm kinematic chain and impedance control, which integrates image-based visual servoing (IBVS). The kinematic decoupling allows for fast end-effector motions and recovery that leads to robust visual servoing. The whole approach and pipeline can be generalized for any mobile platform (wheeled or tracked vehicles), but is most suitable for dynamically moving quadruped manipulators thanks to their reactivity against disturbances. The compliance of the impedance controller makes the robot safer for interactions with humans and the environment. We demonstrate the performance and robustness of the proposed approach with various experiments on our 140 kg HyQReal quadruped robot equipped with a 7-DoF manipulator arm. The experiments consider dynamic locomotion, tracking under external disturbances, and fast motions of the target object.

cs.RO↗

arXiv2023

Control of a Back-Support Exoskeleton to Assist Carrying Activities

Back-support exoskeletons are commonly used in the workplace to reduce low back pain risk for workers performing demanding activities. However, for the assistance of tasks differing from lifting, back-support exoskeletons potential has not been exploited extensively. This work focuses on the use of an active back-support exoskeleton to assist carrying. Two control strategies are designed that modulate the exoskeleton torques to comply with the task assistance requirements. In particular, two gait phase detection frameworks are exploited to adapt the assistance according to the legs' motion. The two strategies are assessed through an experimental analysis on ten subjects. Carrying task is performed without and with the exoskeleton assistance. Results prove the potential of the presented controls in assisting the task without hindering the gait movement and improving the usability experienced by users. Moreover, the exoskeleton assistance significantly reduces the lumbar load associated with the task, demonstrating its promising use for risk mitigation in the workplace.

cs.RO↗

arXiv2022

Learning Skills from Demonstrations: A Trend from Motion Primitives to Experience Abstraction

The uses of robots are changing from static environments in factories to encompass novel concepts such as Human-Robot Collaboration in unstructured settings. Pre-programming all the functionalities for robots becomes impractical, and hence, robots need to learn how to react to new events autonomously, just like humans. However, humans, unlike machines, are naturally skilled in responding to unexpected circumstances based on either experiences or observations. Hence, embedding such anthropoid behaviours into robots entails the development of neuro-cognitive models that emulate motor skills under a robot learning paradigm. Effective encoding of these skills is bound to the proper choice of tools and techniques. This paper studies different motion and behaviour learning methods ranging from Movement Primitives (MP) to Experience Abstraction (EA), applied to different robotic tasks. These methods are scrutinized and then experimentally benchmarked by reconstructing a standard pick-n-place task. Apart from providing a standard guideline for the selection of strategies and algorithms, this paper aims to draw a perspectives on their possible extensions and improvements

cs.RO↗

arXiv2022

A Whole-Body Controller Based on a Simplified Template for Rendering Impedances in Quadruped Manipulators

Quadrupedal manipulators require to be compliant when dealing with external forces during autonomous manipulation, tele-operation or physical human-robot interaction. This paper presents a whole-body controller that allows for the implementation of a Cartesian impedance control to coordinate tracking performance and desired compliance for the robot base and manipulator arm. The controller is formulated through an optimization problem using Quadratic Programming (QP) to impose a desired behavior for the system while satisfying friction cone constraints, unilateral force constraints, joint and torque limits. The presented strategy decouples the arm and the base of the platform, enforcing the behavior of a linear double-mass spring damper system, and allows to independently tune their inertia, stiffness and damping properties. The control architecture is validated through an extensive simulation study using the 90kg HyQ robot equipped with a 7-DoF manipulator arm. Simulation results show the impedance rendering performance when external forces are applied at the arm's end-effector. The paper presents results for full stance condition (all legs on the ground) and, for the first time, also shows how the impedance rendering is affected by the contact conditions during a dynamic gait.

cs.RO↗

arXiv2022

Formulating Intuitive Stack-of-Tasks using Visuo-Tactile Perception for Collaborative Human-Robot Fine Manipulation

Enabling robots to work in close proximity to humans necessitates a control framework that does not only incorporate multi-sensory information for autonomous and coordinated interactions but also has perceptive task planning to ensure an adaptable and flexible collaborative behaviour. In this research, an intuitive stack-of-tasks (iSoT) formulation is proposed, that defines the robot's actions by considering the human-arm postures and the task progression. The framework is augmented with visuo-tactile information to effectively perceive the collaborative environment and intuitively switch between the planned sub-tasks. The visual feedback from depth cameras monitors and estimates the objects' poses and human-arm postures, while the tactile data provides the exploration skills to detect and maintain the desired contacts to avoid object slippage. To evaluate the performance, effectiveness and usability of the proposed framework, assembly and disassembly tasks, performed by the human-human and human-robot partners, are considered and analyzed using distinct evaluation metrics i.e, approach adaptation, grasp correction, task coordination latency, cumulative posture deviation, and task repeatability.

cs.RO↗

arXiv2021

Fusing Visuo-Tactile Perception into Kernelized Synergies for Robust Grasping and Fine Manipulation of Non-rigid Objects

Handling non-rigid objects using robot hands necessities a framework that does not only incorporate human-level dexterity and cognition but also the multi-sensory information and system dynamics for robust and fine interactions. In this research, our previously developed kernelized synergies framework, inspired from human behaviour on reusing same subspace for grasping and manipulation, is augmented with visuo-tactile perception for autonomous and flexible adaptation to unknown objects. To detect objects and estimate their poses, a simplified visual pipeline using RANSAC algorithm with Euclidean clustering and SVM classifier is exploited. To modulate interaction efforts while grasping and manipulating non-rigid objects, the tactile feedback using T40S shokac chip sensor, generating 3D force information, is incorporated. Moreover, different kernel functions are examined in the kernelized synergies framework, to evaluate its performance and potential against task reproducibility, execution, generalization and synergistic re-usability. Experiments performed with robot arm-hand system validates the capability and usability of upgraded framework on stably grasping and dexterously manipulating the non-rigid objects.

cs.RO↗

arXiv2021

Intuitive Tasks Planning Using Visuo-Tactile Perception for Human Robot Cooperation

Designing robotic tasks for co-manipulation necessitates to exploit not only proprioceptive but also exteroceptive information for improved safety and autonomy. Following such instinct, this research proposes to formulate intuitive robotic tasks following human viewpoint by incorporating visuo-tactile perception. The visual data using depth cameras surveils and determines the object dimensions and human intentions while the tactile sensing ensures to maintain the desired contact to avoid slippage. Experiment performed on robot platform with human assistance under industrial settings validates the performance and applicability of proposed intuitive task formulation.

eess.SY↗

arXiv2021

Geometry-aware Manipulability Learning, Tracking and Transfer

Body posture influences human and robots performance in manipulation tasks, as appropriate poses facilitate motion or force exertion along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or apply a specific force. In this context, this paper presents a novel \emph{manipulability transfer} framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.

cs.RO↗