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Learning Humanoid Locomotion with Perceptive Internal Model

In contrast to quadruped robots that can navigate diverse terrains using a "blind" policy, humanoid robots require accurate perception for stable locomotion due to their high degrees of free...

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This article presents a novel approach to humanoid locomotion that effectively incorporates perception while addressing stability and robustness challenges. The proposed Perceptive Internal Model (PIM) demonstrates a high level of innovation with its reliance on elevation maps and minimal computational overhead. The rigorous validation across multiple terrains and robots further enhances its methodological rigor. Its implications for future humanoid robot development signify high relevance.

Baking Gaussian Splatting into Diffusion Denoiser for Fast and Scalable Single-stage Image-to-3D Generation

Existing feed-forward image-to-3D methods mainly rely on 2D multi-view diffusion models that cannot guarantee 3D consistency. These methods easily collapse when changing the prompt view direction and ...

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The article presents a significant advancement in the field of single-stage image-to-3D generation by addressing limitations in existing methods regarding 3D consistency and view variability. The novelty of introducing a diffusion model that generates 3D point clouds directly is particularly impactful, potentially setting a new standard for efficiency and quality in the area. The rigorous data-backed comparisons to state-of-the-art methods support strong claims of improved performance, while the practical applications enhance its relevance further.

Not Just a Dot: the complex UV morphology and underlying properties of Little Red Dots

We analyze 99 photometrically selected Little Red Dots (LRDs) at z ~ 4-8 in the GOODS fields, leveraging ultra-deep JADES NIRCam short-wavelength (SW) data. We examine the morphology of 30 LRDs; the r...

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The article offers valuable insights into the complex morphology and properties of Little Red Dots, addressing a niche yet significant area of exploration using rigorous data analysis from recent advanced instrumentation. The findings concerning the mixture of AGN activity with stellar processes, and the detailed morphological analysis based on robust spectroscopic data, emphasize both its novelty and potential impact on subsequent studies surrounding galaxy formation and evolution.

Sampling Observability for Heat Equations with Memory

This paper studies the sampling observability for the heat equations with memory in the lower-order term, where the observation is conducted at a finite number of time instants and on a small open sub...

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The article presents a novel approach to understanding sampling observability that extends traditional heat equation frameworks by incorporating memory effects. This advancement in method design and the identification of conditions related to the memory kernel highlight its potential for deeper insights in observability theory. The rigor in establishing the two-sided inequality enhances the robustness of the findings, making it a valuable contribution to the field.

ETA-IK: Execution-Time-Aware Inverse Kinematics for Dual-Arm Systems

This paper presents ETA-IK, a novel Execution-Time-Aware Inverse Kinematics method tailored for dual-arm robotic systems. The primary goal is to optimize motion execution time by leveraging the redund...

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The article introduces a novel approach to inverse kinematics with a strong focus on execution time optimization, a critical aspect in robotics. Its methodological rigor, demonstrated through experimental validation with two widely used robotic arms, underscores the reliability of the results. The incorporation of neural networks for time approximation adds a contemporary and advanced edge, enhancing its applicability. The findings have immediate implications for various practical robotic applications, reinforcing its relevance in the field.

Modified gravity from Weyl connection and the $f(R,\cal{A})$ extension

We use Weyl connection and Weyl geometry in order to construct novel modified gravitational theories. In the simplest case where one uses only the Weyl-connection Ricci scalar as a Lagrangian, the the...

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The article presents a novel approach to modified gravity theories using Weyl connection, which is a significant advancement in understanding gravitational frameworks beyond general relativity. Its rigorous methodological approach and the ability to offer new insights into dark energy and cosmology increase its impact. The ability to recover established models while also exploring new dynamic behaviors adds to its relevance.

Rationality of singular cubic threefolds over $\mathbb R$

We study rationality properties of real singular cubic threefolds.

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The study of rationality properties of singular cubic threefolds presents a meaningful contribution to algebraic geometry, particularly in the context of understanding real versus complex varieties. The focus on singularities adds a layer of complexity that is likely to provoke further inquiry and encourage the exploration of related geometric structures. Although the topic may be quite specialized, it has the potential for broader applicability and influence within the field, particularly regarding the connections with birational geometry and the theory of rationality in higher-dimensional varieties.

CoNFiLD-inlet: Synthetic Turbulence Inflow Using Generative Latent Diffusion Models with Neural Fields

Eddy-resolving turbulence simulations require stochastic inflow conditions that accurately replicate the complex, multi-scale structures of turbulence. Traditional recycling-based methods rely on comp...

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The article presents a novel deep learning model that addresses the limitations of existing synthetic turbulence inflow methods, making a significant contribution to the field of fluid dynamics. Its integration of generative latent diffusion models and neural fields is not only innovative but also extends its applicability across various Reynolds numbers without the need for retraining. This methodological rigor, alongside comprehensive validation, enhances its credibility and potential impact on future research into turbulence modeling.

The constraining power of the Marked Power Spectrum: an analytical study

The marked power spectrum - a two-point correlation function of a transformed density field - has emerged as a promising tool for extracting cosmological information from the large-scale structure of ...

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This article presents a novel analytical study on the marked power spectrum, which shows promise in extracting cosmological information and is particularly relevant for future research in primordial non-Gaussianity. Its methodological rigor, including validation against simulations and systematic analysis, enhances its relevance. The comprehensive nature of the study and its implications for upcoming large-volume surveys contribute to its high impact potential.

Solutions to the minimal surface system with large singular sets

Lawson and Osserman proved that the Dirichlet problem for the minimal surface system is not always solvable in the class of Lipschitz maps. However, it is known that minimizing sequences (for area) of...

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The article addresses a significant gap in the understanding of the convergence behavior of minimizing sequences in the minimal surface system, particularly highlighting the existence of large singular sets. This novel insight could potentially change the interpretation of areas in higher codimension, making it relevant for ongoing research in geometric analysis and minimal surface theory. Additionally, the focus on minimal dimensions adds a layer of specificity that can inspire various applications and methodologies in related fields.

Model Checking for Reinforcement Learning in Autonomous Driving: One Can Do More Than You Think!

Most reinforcement learning (RL) platforms use high-level programming languages, such as OpenAI Gymnasium using Python. These frameworks provide various API and benchmarks for testing RL algorithms in...

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This article presents a novel approach by combining model checking (MC) with reinforcement learning (RL) in the context of autonomous driving (AD). The paper addresses a significant gap in the existing literature by focusing on the correctness of models and reward functions, which are crucial for the reliability of RL systems. The methodological rigor demonstrated in the experiments enhances its credibility. Moreover, the dual applicability of MC for pre-analysis and reward design can influence future research significantly by encouraging more robust RL frameworks in AD.

Using Formal Models, Safety Shields and Certified Control to Validate AI-Based Train Systems

The certification of autonomous systems is an important concern in science and industry. The KI-LOK project explores new methods for certifying and safely integrating AI components into autonomous tra...

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The article presents a novel approach to certifying AI systems in autonomous trains, which is a critical area in the development of safe and reliable transportation systems. Its dual-layered strategy combining formal analysis with runtime checking and the integration of a demonstrator for real-world application enhances its impact. This methodological rigor and applicability to an emerging field make the work highly relevant.

Cross--layer Formal Verification of Robotic Systems

Robotic systems are widely used to interact with humans or to perform critical tasks. As a result, it is imperative to provide guarantees about their behavior. Due to the modularity and complexity of ...

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The article addresses a critical issue in robotic systems by proposing a novel cross-layer verification method, which is highly relevant given the increasing complexity of such systems. Its methodological rigor is enhanced by the innovative approach of using abstractions of other layers to verify system properties, which could lead to more reliable robot designs. The focus on avoiding the state-space explosion problem is particularly significant, as it is a common challenge in systems verification. Overall, its high potential for practical application in safety-critical domains merits a strong relevance score.

A Case Study on Numerical Analysis of a Path Computation Algorithm

Lack of numerical precision in control software -- in particular, related to trajectory computation -- can lead to incorrect results with costly or even catastrophic consequences. Various tools have b...

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This article addresses a critical issue of numerical precision in control software, which has significant implications for various applications, especially in safety-critical systems. The focus on an industrial implementation and the presentation of practical results add substantial value to both the theoretical understanding and real-world applicability of path computation algorithms. The methodological rigor and future work directions could inspire further research in precision analysis and algorithm development.

Synthesising Robust Controllers for Robot Collectives with Recurrent Tasks: A Case Study

When designing correct-by-construction controllers for autonomous collectives, three key challenges are the task specification, the modelling, and its use at practical scale. In this paper, we focus o...

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The article presents a rigorous approach to synthesising controllers specifically for robot collectives while addressing significant challenges in task specification, modelling, and scalability. The focus on attaining robustness against environmental uncertainty and providing actionable insights for POMDP modelling enhances its practical relevance and impact on the field of robotics. The case study application broadens its appeal, informing future practical implementations. However, while the proposed methods are innovative, their applicability to broader contexts beyond the specific case study (cleaning robots) could be further clarified.

Convergence and Stability Analysis of the Extended Infinite Horizon Model Predictive Control

Model Predictive Control (MPC) is a popular technology to operate industrial systems. It refers to a class of control algorithms that use an explicit model of the system to obtain the control action b...

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The article offers robust theoretical insights into the convergence and stability of Model Predictive Control (MPC), two critical aspects of control systems. The focus on extending existing models to provide a more general and rigorous basis for finite horizon MPC contributes to the ongoing discourse in control theory. The application of mathematical proofs and the foundations laid for future adaptations enhance its novelty and potential impact on further research.

Model Checking and Verification of Synchronisation Properties of Cobot Welding

This paper describes use of model checking to verify synchronisation properties of an industrial welding system consisting of a cobot arm and an external turntable. The robots must move synchronously,...

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The article presents a robust application of model checking in industrial robotics, providing a concrete example of how formal methods can solve practical synchronization issues. The demonstration of significant improvements in welding quality through verification techniques highlights both the novelty and applicability of the research. However, further exploration of broader applicability and comparative case studies could enhance its impact.

RV4Chatbot: Are Chatbots Allowed to Dream of Electric Sheep?

Chatbots have become integral to various application domains, including those with safety-critical considerations. As a result, there is a pressing need for methods that ensure chatbots consistently a...

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The novelty of the RV4Chatbot framework addresses a critical gap in chatbot safety and compliance in high-stakes environments, making it highly relevant. The methodological rigor is demonstrated through practical implementations and real-world experiments, lending credibility to its effectiveness. The framework's applicability to two widely-used chatbot platforms enhances its potential for broad adoption and future research inquiry.

ROSMonitoring 2.0: Extending ROS Runtime Verification to Services and Ordered Topics

Formal verification of robotic applications presents challenges due to their hybrid nature and distributed architecture. This paper introduces ROSMonitoring 2.0, an extension of ROSMonitoring designed...

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The article introduces ROSMonitoring 2.0, a significant update to existing runtime verification methodologies for robotic operating systems, particularly addressing previously unmonitored aspects such as services and ordered topics. The enhancements in real-time capabilities and the application to a practical case study (fire-fighting UAV) indicate strong methodological rigor and applicability. Moreover, the focus on scalability and interoperability is timely given the increasing complexity of robotics applications, making it highly relevant for researchers and practitioners in the field.

Direct-photon production in inelastic and high-multiplicity proton-proton collisions at $\sqrt{s} =$ 13 TeV

In this letter, we present the first measurement of direct photons at the transverse momentum of 1 < p_{\rm T} < 6 GeV/ $c$ at midrapidity |η| < 0.8 in inelastic a...

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The article presents a novel measurement of direct photon production in high-energy proton-proton collisions, which is significant for understanding photon production mechanisms in particle physics. The research is methodologically rigorous and provides valuable data that can challenge and refine perturbative QCD models. Its implications for higher-order calculations and future experiments add to its relevance.