arXiv subjects

Sigrid Adriaenssens

Publications and source records attributed to Sigrid Adriaenssens.

11 recordsLinked to original sources

arXiv2026

Learning Diffusion Policies for Robotic Manipulation of Timber Joinery under Fabrication Uncertainty

Fabrication uncertainty arising from tolerance accumulation, material imperfection, and positioning errors remains a critical barrier to automated robotic assembly in construction, particularly for contact-rich manipulation tasks under minimal geometric clearance. This paper investigates the deployment of diffusion policy learning on construction-scale industrial robots to enable robust, high-precision assembly under such uncertainty, using tight-clearance mortise and tenon timber joinery as a representative case study. Sensory-motor diffusion policies are trained using teleoperated demonstrations collected from an industrial robotic workcell equipped with force/torque sensing. A two-phase experimental study evaluates baseline performance and robustness under randomized positional perturbations up to 10 mm, far exceeding the joint clearance. The best-performing policy achieved 100% success under nominal conditions and 75% average success under uncertainty. These results suggest that diffusion policies can improve robustness to fabrication-induced misalignment, representing a step toward reliable robotic assembly in construction under tight tolerances.

cs.RO↗

arXiv2026

A Latency-Aware Framework for Visuomotor Policy Learning on Industrial Robots

Industrial robots are increasingly deployed in construction and manufacturing tasks, where the deployment of end-to-end visuomotor policies is challenged by the observation-execution gap induced by observation, inference, and execution latencies. This gap is often significant on industrial robotic arms due to high-level control interfaces and slower closed-loop dynamics, making execution timing a dominant system-level concern. This paper presents a system-level, latency-aware framework for deploying and evaluating visuomotor policies on industrial robotic arms. The framework integrates latency-calibrated multimodal sensing, data synchronization, a unified communication pipeline, and a teleoperation interface for collecting expert demonstrations. Within this framework, we formalize a latency-aware execution strategy that assigns timestamps to policy-predicted action sequences and schedules only temporally feasible actions according to their intended execution time, enabling asynchronous inference and execution without modifying policy architectures or training procedures. We evaluate the framework on a contact-rich assembly task while systematically varying inference latency and compare latency-aware execution against blocking and naive asynchronous baselines using identical policies and sensing modalities. Results show that latency-aware execution preserves smooth motion, compliant contact behavior, and task progression consistent with demonstrations across inference latencies of 100-500 ms. Latency-aware execution maintained task duration and motion smoothness within 13% and 9% of the demonstration reference, respectively, while avoiding the latency-dependent slowdown observed under blocking execution and the large contact-force overshoots produced by naive asynchronous execution.

cs.RO↗

arXiv2026

Hyper-reduction methods for accelerating nonlinear finite element simulations: open source implementation and reproducible benchmarks

Hyper-reduction methods have gained increasing attention for their potential to accelerate reduced order models for nonlinear systems, yet their comparative accuracy and computational efficiency are not well understood. Motivated by this gap, we evaluate a range of hyper-reduction techniques for nonlinear finite element models across benchmark problems of varying complexity, assessing the inevitable tradeoff between accuracy and speedup. More specifically, we consider interpolation methods based on the gappy proper orthogonal decomposition as well as the empirical quadrature procedure (EQP), and apply them to the hyper-reduction of problems in nonlinear diffusion, nonlinear elasticity and Lagrangian hydrodynamics. Our numerical results are generated using the open source libROM, Laghos and MFEM numerical libraries. Our findings reveal that the comparative performance between hyper-reduction methods depends on both the problem and the choice of time integration method. The EQP method generally achieves lower relative errors than interpolation methods and is more efficient in terms of quadrature point usage, resulting in a lower wall time for the nonlinear diffusion and elasticity problems. However, its online computational cost is observed to be relatively high for Lagrangian hydrodynamics problems. Conversely, interpolation methods exhibit greater variability, especially with respect to the use of different time integration methods in the Lagrangian hydrodynamics problems. The presented results underscore the need for problem specific method selection to balance accuracy and efficiency, while also offering useful guidance for future comparisons and refinements of hyper-reduction techniques.

cs.MS↗

arXiv2026

Physics-informed neural networks for form-finding of unilateral membrane structures

Form-finding of unilateral membrane structures is commonly addressed by solving equilibrium equations with Finite Element Methods (FEMs). This paper investigates Physics-Informed Neural Networks (PINNs) as an alternative, where the equilibrium equation is enforced by minimizing its residual at collocation points during neural-network training rather than by solving a mesh-based discretized system. This approach is well suited to form-finding problems based on Membrane Equilibrium Analysis (MEA), in which the unknown membrane surface is governed by a second-order elliptic Partial Differential Equation (PDE) with Dirichlet boundary conditions. Two PINN formulations are proposed and compared: a soft-Boundary Condition (soft-BC) approach, where the boundary conditions are imposed through a penalty term, and a hard-BC approach, where they are satisfied exactly by construction through distance and lift functions. The methods are assessed on three case studies with different geometrical complexity, including compression-only and tension-only stress states, and combined self-weight, concentrated vertical loads, and horizontal actions. Both formulations produce membrane surfaces in close agreement with solutions obtained using an FEM-based PDE solver. The hard-BC formulation gives smaller errors and a smoother residual distribution, especially near the boundary, showing that exact enforcement of the Dirichlet conditions improves overall accuracy. The soft-BC formulation still provides structurally meaningful solutions and remains attractive when simpler implementation is preferred and limited relaxation of the boundary data is acceptable. Overall, the results show that PINNs are a viable alternative for MEA-based form-finding.

cs.CE↗

arXiv2025

Real-time design of architectural structures with differentiable mechanics and neural networks

Designing mechanically efficient geometry for architectural structures like shells, towers, and bridges, is an expensive iterative process. Existing techniques for solving such inverse problems rely on traditional optimization methods, which are slow and computationally expensive, limiting iteration speed and design exploration. Neural networks would seem to offer a solution via data-driven amortized optimization, but they often require extensive fine-tuning and cannot ensure that important design criteria, such as mechanical integrity, are met. In this work, we combine neural networks with a differentiable mechanics simulator to develop a model that accelerates the solution of shape approximation problems for architectural structures represented as bar systems. This model explicitly guarantees compliance with mechanical constraints while generating designs that closely match target geometries. We validate our approach in two tasks, the design of masonry shells and cable-net towers. Our model achieves better accuracy and generalization than fully neural alternatives, and comparable accuracy to direct optimization but in real time, enabling fast and reliable design exploration. We further demonstrate its advantages by integrating it into 3D modeling software and fabricating a physical prototype. Our work opens up new opportunities for accelerated mechanical design enhanced by neural networks for the built environment.

cs.CE↗

arXiv2024

Kirigami-inspired wind steering for natural ventilation

Ensuring adequate ventilation of exterior and interior urban spaces is essential for the safety and comfort of inhabitants. Here, we examine how angled features can steer wind into areas with stagnant air, promoting natural ventilation. Using Large Eddy Simulations (LES) and wind tunnel experiments with particle image velocimetry (PIV) measurements, we first examine how louvers, located at the top of a box enclosed on four sides, can improve ventilation in the presence of incoming wind. By varying louver scale, geometry, and angle, we identify a geometric regime wherein louvers capture free-stream air to create sweeping interior flow structures, increasing the Air Exchange Rate (ACH) significantly above that for an equivalent box with an open top. We then show that non-homogeneous louver orientations enhance ventilation, accommodating winds from opposing directions, and address the generalization to taller structures. Finally, we demonstrate the feasibility of replacing louvers with lattice-cut kirigami ("cut paper"), which forms angled chutes when stretched in one direction, and could provide a mechanically preferable solution for adaptive ventilation. Our findings for this idealized system may inform the design of retrofits for urban structures -- e.g. canopies above street canyons, and "streeteries" or parklets -- capable of promoting ventilation, while simultaneously providing shade.

physics.flu-dyn↗

arXiv2023

JAX FDM: A differentiable solver for inverse form-finding

We introduce JAX FDM, a differentiable solver to design mechanically efficient shapes for 3D structures conditioned on target architectural, fabrication and structural properties. Examples of such structures are domes, cable nets and towers. JAX FDM solves these inverse form-finding problems by combining the force density method, differentiable sparsity and gradient-based optimization. Our solver can be paired with other libraries in the JAX ecosystem to facilitate the integration of form-finding simulations with neural networks. We showcase the features of JAX FDM with two design examples. JAX FDM is available as an open-source library at https://github.com/arpastrana/jax_fdm.

cs.CE↗

arXiv2023

Continuous modeling of creased annuli with tunable bistable and looping behaviors

Creases are purposely introduced to thin structures for designing deployable origami, artistic geometries, and functional structures with tunable nonlinear mechanics. Modeling the mechanics of creased structures is challenging because creases introduce geometric discontinuity and often have complex mechanical responses due to the local material damage. In this work, we propose a continuous description of the sharp geometry of creases and apply it to the study of \emph{creased annuli}, made by introducing radial creases to annular strips with the creases annealed to behave elastically. We find creased annuli have generic bistability and can be folded into various compact shapes, depending on the crease pattern and the overcurvature of the flat annulus. We use a regularized Dirac delta function (RDDF) to describe the geometry of a crease, with the finite spike of the RDDF capturing the localized curvature. Together with anisotropic rod theory, we solve the nonlinear mechanics of creased annuli, with its stability determined by the standard conjugate point test. We find excellent agreement between precision tabletop models, numerical predictions from our analytical framework, and modeling results from finite element simulations. We further show that by varying the rest curvature of the thin strip, dynamic switches between different states of creased annuli can be achieved, which could inspire the design of novel deployable and morphable structures. We believe our smooth description of discontinuous geometries will benefit to the mechanical modeling and design of a wide spectrum of engineering structures that embrace geometric and material discontinuities.

cond-mat.soft↗

arXiv2022

Learning the nonlinear dynamics of soft mechanical metamaterials with graph networks

The dynamics of soft mechanical metamaterials provides opportunities for many exciting engineering applications. Previous studies often use discrete systems, composed of rigid elements and nonlinear springs, to model the nonlinear dynamic responses of the continuum metamaterials. Yet it remains a challenge to accurately construct such systems based on the geometry of the building blocks of the metamaterial. In this work, we propose a machine learning approach to address this challenge. A metamaterial graph network (MGN) is used to represent the discrete system, where the nodal features contain the positions and orientations the rigid elements, and the edge update functions describe the mechanics of the nonlinear springs. We use Gaussian process regression as the surrogate model to characterize the elastic energy of the nonlinear springs as a function of the relative positions and orientations of the connected rigid elements. The optimal model can be obtained by "learning" from the data generated via finite element calculation over the corresponding building block of the continuum metamaterial. Then, we deploy the optimal model to the network so that the dynamics of the metamaterial at the structural scale can be studied. We verify the accuracy of our machine learning approach against several representative numerical examples. In these examples, the proposed approach can significantly reduce the computational cost when compared to direct numerical simulation while reaching comparable accuracy. Moreover, defects and spatial inhomogeneities can be easily incorporated into our approach, which can be useful for the rational design of soft mechanical metamaterials.

cs.CE↗

arXiv2021

Numerical modeling of static equilibria and bifurcations in bigons and bigon rings

In this study, we explore the mechanics of a bigon and a bigon ring from a combination of experiments and numerical simulations. A bigon is a simple elastic network consisting of two initially straight strips that are deformed to intersect with each other through a fixed intersection angle at each end. A bigon ring is a novel multistable structure composed of a series of bigons arranged to form a loop. We find that a bigon ring usually contains several families of stable states and one of them is a multiply-covered loop, which is similar to the folding behavior of a bandsaw blade. To model bigons and bigon rings, we propose a numerical framework combining several existing techniques to study mechanics of elastic networks consisting of thin strips. Each strip is modeled as a Kirchhoff rod, and the entire strip network is formulated as a two-point boundary value problem (BVP) that can be solved by a general-purpose BVP solver. Together with numerical continuation, we apply the numerical framework to study static equilibria and bifurcations of the bigons and bigon rings. Both numerical and experimental results show that the intersection angle and the aspect ratio of the strip's cross section contribute to the bistability of a bigon and the multistability of a bigon ring; the latter also depends on the number of bigon cells in the ring. The numerical results further reveal interesting connections among various stable states in a bigon ring. Our numerical framework can be applied to general elastic rod networks that may contain flexible joints, naturally curved strips of different lengths, etc. The folding and multistable behaviors of a bigon ring may inspire the design of novel deployable and morphable structures

cond-mat.soft↗

arXiv2021

Three Cooperative Robotic Fabrication Methods for the Scaffold-Free Construction of a Masonry Arch

Geometrically complex masonry structures (e.g., arches, domes, vaults) are traditionally built with expensive scaffolding or falsework to provide stability during construction. The process of building such structures can potentially be improved through the use of multiple robots working together in a cooperative assembly framework. Here a robot is envisioned as both a placement and external support agent during fabrication - the unfinished structure is supported in such a way that scaffolding is not required. The goal of this paper is to present and validate the efficacy of three cooperative fabrication approaches using two or three robots, for the scaffold-free construction of a stable masonry arch from which a medium-span vault is built. A simplified numerical method to represent a masonry structure is first presented and validated to analyse systems composed of discrete volumetric elements. This method is then used to evaluate the effect of the three cooperative robotic fabrication strategies on the stability performance of the central arch. The sequential method and cantilever method, which utilize two robotic arms, are shown to be viable methods, but have challenges related to scalability and robustness. By adding a third robotic agent, it becomes possible to determine a structurally optimal fabrication sequence through a multi-objective optimization process. The optimized three robot method is shown to significantly improve the structural behavior over all fabrication steps. The modeling approaches presented in this paper are broadly formulated and widely applicable for the analysis of cooperative robotic fabrication sequences for the construction of discrete element structures across scales and materials.

cs.RO↗