How can we make global sensitivity analysis accessible and viable for engineering practice? In this translation article, we present a methodology to enable sensitivity analysis for structural and geotechnical engineering for built environment design and assessment workflows. Our technique wraps computational mechanics and geomechanics finite element (FE) simulations and combines high-performance computing on public cloud with surrogate modeling using machine learning. A key question we address is: “Is there a noticeable loss in fidelity of results from the sensitivity analysis when substituting a simulation model with a surrogate model?” We answer this question for both linear and nonlinear FE simulations.

Robotic manipulation inherently involves contact with objects for task accomplishment. Traditional motion planning techniques, while having shown their success in collision-free scenarios, may not handle manipulation tasks effectively because they typically avoid contact. Although geometric constraints have been introduced into classical motion planners for tasks that involve interactions, they still lack the capability to fully incorporate contact. In addition, these planning methods generally do not operate on objects that cannot be directly controlled. In this work, building on a recently proposed framework for energy-based quasi-static manipulation, we propose an approach to manipulation planning by adapting a numerical continuation algorithm to compute the equilibrium manifold (EM), which is implicitly derived from physical laws. By defining a manipulation potential energy function that captures interaction and natural potentials, the numerical continuation approach is integrated with adaptive ordinary differential equations that converge to the EM. This allows discretizing the implicit manifold as a graph with a finite set of equilibria as nodes interconnected by weighted edges defined via a haptic metric. The proposed framework is evaluated with an inverted pendulum task, where the explored branch of the manifold demonstrates effectiveness.

Metal–organic polyhedra (MOPs) are discrete, porous metal–organic assemblies known for their wide-ranging applications in separation, drug delivery, and catalysis. As part of The World Avatar (TWA) project—a universal and interoperable knowledge model—we have previously systematized known MOPs and expanded the explorable MOP space with novel targets. Although these data are available via a complex query language, a more user-friendly interface is desirable to enhance accessibility. To address a similar challenge in other chemistry domains, the natural language question-answering system “Marie” has been developed; however, its scalability is limited due to its reliance on supervised fine-tuning, which hinders its adaptability to new knowledge domains. In this article, we introduce an enhanced database of MOPs and a first-of-its-kind question-answering system tailored for MOP chemistry. By augmenting TWA’s MOP database with geometry data, we enable the visualization of not just empirically verified MOP structures but also machine-predicted ones. In addition, we renovated Marie’s semantic parser to adopt in-context few-shot learning, allowing seamless interaction with TWA’s extensive MOP repository. These advancements significantly improve the accessibility and versatility of TWA, marking an important step toward accelerating and automating the development of reticular materials with the aid of digital assistants.

Robots need a sense of touch to handle objects effectively, and force sensors provide a straightforward way to measure touch or physical contact. However, contact force data are typically sparse and difficult to analyze, as it only appears during contact and is often affected by noise. Therefore, many researchers have consequently relied on vision-based methods for robotic manipulation. However, vision has limitations, such as occlusions that block the camera’s view, making it ineffective or insufficient for dexterous tasks involving contact. This article presents a method for robotic systems operating under quasi-static conditions to perform contact-rich manipulation using only force/torque measurements. First, the interaction forces/torques between the manipulated object and its environment are collected in advance. A potential function is then constructed from the collected force/torque data using Gaussian process regression with derivatives. Next, we develop haptic dynamic movement primitives (Haptic DMPs) to generate robot trajectories. Unlike conventional DMPs, which primarily focus on kinematic aspects, our Haptic DMPs incorporate force-based interactions by integrating the constructed potential energy. The effectiveness of the proposed method is demonstrated through numerical tasks, including the classical peg-in-hole problem.