Tootfinder

PA-MPPI: Perception-Aware Model Predictive Path Integral Control for Quadrotor Navigation in Unknown Environments
Yifan Zhai, Rudolf Reiter, Davide Scaramuzza
https://arxiv.org/abs/2509.14978

PA-MPPI: Perception-Aware Model Predictive Path Integral Control for Quadrotor Navigation in Unknown Environments
Quadrotor navigation in unknown environments is critical for practical missions such as search-and-rescue. Solving it requires addressing three key challenges: the non-convexity of free space due to obstacles, quadrotor-specific dynamics and objectives, and the need for exploration of unknown regions to find a path to the goal. Recently, the Model Predictive Path Integral (MPPI) method has emerged as a promising solution that solves the first two challenges. By leveraging sampling-based optimiz…

https://www.instagram.com/p/DL-3K2JKRhY/?utm_source=ig_web_copy_link

Jacob Bauer (Philosopher Games) on Instagram: "Give thanks 🙌 (Meme submitted by Amelia Sugarbaker, original creator unknown)"
7,142 likes, 60 comments - philosophergames on July 11, 2025: "Give thanks 🙌 (Meme submitted by Amelia Sugarbaker, original creator unknown)".

Active Fault Identification and Robust Control for Unknown Bounded Faults via Volume-Based Costs
Annalena Daniels, Johannes Teutsch, Fabian Kleindienst, Marion Leibold, Dirk Wollherr
https://arxiv.org/abs/2508.12010

Active Fault Identification and Robust Control for Unknown Bounded Faults via Volume-Based Costs
This paper proposes a novel framework for active fault diagnosis and parameter estimation in linear systems operating in closed-loop, subject to unknown but bounded faults. The approach integrates set-membership identification with a cost function designed to accelerate fault identification. Informative excitation is achieved by minimizing the size of the parameter uncertainty set, which is approximated using ellipsoidal outer bounds. Combining this formulation with a scheduling parameter enabl…

wordnet: WordNet relationships
A network of English words from the WordNet. Node is a word, and edge denotes relationships between words (synonymy, hyperonymy, meronymy, etc.). The date at which this network was extracted from WordNet is not unknown.
This network has 146005 nodes and 656999 edges.
Tags: Informational, Language, Unweighted
htt…

A previously unknown regular satellite of #Uranus has been detected in a series of images obtained by the Near-Infrared Camera (NIRCam) onboard the James Webb Space Telescope on 2025 Feb. 2: http://www.cbat.eps.harvard.edu/iau/cbet/005500/CBET005593.txt -> https://science.nasa.gov/blogs/webb/2025/08/19/new-moon-discovered-orbiting-uranus-using-nasas-webb-telescope/ - the object is located at a projected radial distance of 56250 /- 250 km from Uranus' center in the planet's equatorial plane, initial astrometry is consistent with the moon orbiting on a nearly circular orbit with an orbital period 0.402 days, and the observed IR flux from the object indicates a radius of 4 - 5 km, placing it well below the detection threshold of earlier images from Voyager and the Hubble Space Telescope.

Flight Manifests Reveal Dozens of Previously Unknown People on Three Deportation Flights to El Salvador https://www.404media.co/flight-manifests-reveal-dozens-of-previously-unknown-people-on-three-deportation-flights-…

Inference of unknown syndrome values in the implementation of the Berlekamp-Massey-Sakata algorithm
J. J. Bernal, J. J. Sim\'on
https://arxiv.org/abs/2509.14835 https://

Inference of unknown syndrome values in the implementation of the Berlekamp-Massey-Sakata algorithm
We study the problem of finding those missing syndrome values that are needed to implment the Berlekamp-Massey-Sakata algorithm as the Feng-Rao Majority Voting for algebraic geometric codes. We apply our results to solve syndrome correction in abelian codes.

CKM-Assisted Physical-Layer Security for Resilience Against Unknown Eavesdropping Location
Ladan Khaloopour, Matthias Hollick, Vahid Jamali
https://arxiv.org/abs/2508.13681 http…

CKM-Assisted Physical-Layer Security for Resilience Against Unknown Eavesdropping Location
Channel Knowledge Map (CKM) is an emerging data-driven toolbox that captures our awareness of the wireless channel and enables efficient communication and resource allocation beyond the state of the art. In this work, we consider CKM for improving physical-layer security (PLS) in the presence of a passive eavesdropper (Eve), without making any assumptions about Eve's location or channel state information (CSI). We employ highly directional mmWave transmissions, with the confidential message joi…

Erstmals wurden Tiefsee-#Seespinnen entdeckt, die mithilfe von #Methan-basierten #Bakterien überleben.
Sie haben anscheinend auch von

First methane-powered sea spiders found crawling on the ocean floor
Scientists on the US West Coast say they discovered three previously unknown species of deep-sea spider that could have a rare diet fueled by a common greenhouse gas.

Multi-CAP: A Multi-Robot Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments
Zongyuan Shen, Burhanuddin Shirose, Prasanna Sriganesh, Bhaskar Vundurthy, Howie Choset, Matthew Travers
https://arxiv.org/abs/2509.14941

Multi-CAP: A Multi-Robot Connectivity-Aware Hierarchical Coverage Path Planning Algorithm for Unknown Environments
Efficient coordination of multiple robots for coverage of large, unknown environments is a significant challenge that involves minimizing the total coverage path length while reducing inter-robot conflicts. In this paper, we introduce a Multi-robot Connectivity-Aware Planner (Multi-CAP), a hierarchical coverage path planning algorithm that facilitates multi-robot coordination through a novel connectivity-aware approach. The algorithm constructs and dynamically maintains an adjacency graph that …

Anyone identify this bird? (Just seen in Manchester, UK). It looks way too small and ungainly for a heron, but moderately long neck. hmm.

If you'd like to know how my day has been, let's just say I had to get a rust app's binary onto someone's 32-bit Debian 7 (wheezy) machine. Kernel 3.2.0. Version of glibc unsupported by any version of rust.
Ended up cross-building it to i686-unknown-linux-musl from a modern amd64 host.

A cell centered Galerkin method for miscible displacement in heterogeneous porous media
Maurice S. Fabien
https://arxiv.org/abs/2509.14864 https://arxiv.or…

A cell centered Galerkin method for miscible displacement in heterogeneous porous media
In this paper we present a cell centered Galerkin (CCG) method applied to miscible displacement problems in heterogeneous porous media. The CCG approach combines concepts from finite volume and discontinuous Galerkin (DG) methods to arrive at an efficient lowest-order approximation (one unknown per cell). We demonstrate that the CCG method can be defined using classical DG weak formulations, only requires one unknown per cell, and is able to deliver comparable accuracy and improved efficiency o…

Friend or Foe: Delegating to an AI Whose Alignment is Unknown
Drew Fudenberg, Annie Liang
https://arxiv.org/abs/2509.14396 https://arxiv.org/pdf/2509.14396…

Friend or Foe: Delegating to an AI Whose Alignment is Unknown
AI systems have the potential to improve decision-making, but decision makers face the risk that the AI may be misaligned with their objectives. We study this problem in the context of a treatment decision, where a designer decides which patient attributes to reveal to an AI before receiving a prediction of the patient's need for treatment. Providing the AI with more information increases the benefits of an aligned AI but also amplifies the harm from a misaligned one. We characterize how the de…

LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
Chin-Yang Lin, Cheng Sun, Fu-En Yang, Min-Hung Chen, Yen-Yu Lin, Yu-Lun Liu
https://arxiv.org/abs/2508.14041

LongSplat: Robust Unposed 3D Gaussian Splatting for Casual Long Videos
LongSplat addresses critical challenges in novel view synthesis (NVS) from casually captured long videos characterized by irregular camera motion, unknown camera poses, and expansive scenes. Current methods often suffer from pose drift, inaccurate geometry initialization, and severe memory limitations. To address these issues, we introduce LongSplat, a robust unposed 3D Gaussian Splatting framework featuring: (1) Incremental Joint Optimization that concurrently optimizes camera poses and 3D Gau…

Online Bayesian Risk-Averse Reinforcement Learning
Yuhao Wang, Enlu Zhou
https://arxiv.org/abs/2509.14077 https://arxiv.org/pdf/2509.14077

Online Bayesian Risk-Averse Reinforcement Learning
In this paper, we study the Bayesian risk-averse formulation in reinforcement learning (RL). To address the epistemic uncertainty due to a lack of data, we adopt the Bayesian Risk Markov Decision Process (BRMDP) to account for the parameter uncertainty of the unknown underlying model. We derive the asymptotic normality that characterizes the difference between the Bayesian risk value function and the original value function under the true unknown distribution. The results indicate that the Baye…

State-to-Hamiltonian conversion with a few copies
Kaito Wada, Jumpei Kato, Hiroyuki Harada, Naoki Yamamoto
https://arxiv.org/abs/2509.14791 https://arxiv.o…

State-to-Hamiltonian conversion with a few copies
Density matrix exponentiation (DME) is a general procedure that converts an unknown quantum state into the Hamiltonian evolution. This enables state-dependent operations and can reveal nontrivial properties of the state, among other applications, without full tomography. However, it has been proven that for any physical process, the DME requires $Θ(1/\varepsilon)$ state copies in error $\varepsilon$. In this work, we go beyond the lower bound and propose a procedure called the virtual DME that…

DEFENDCLI: {Command-Line} Driven Attack Provenance Examination
Peilun Wu, Nan Sun, Nour Moustafa, Youyang Qu, Ming Ding
https://arxiv.org/abs/2508.12553 https://

DEFENDCLI: {Command-Line} Driven Attack Provenance Examination
Endpoint Detection and Response (EDR) solutions embrace the method of attack provenance graph to discover unknown threats through system event correlation. However, this method still faces some unsolved problems in the fields of interoperability, reliability, flexibility, and practicability to deliver actionable results. Our research highlights the limitations of current solutions in detecting obfuscation, correlating attacks, identifying low-frequency events, and ensuring robust context awaren…

RPKT: Learning What You Don't -- Know Recursive Prerequisite Knowledge Tracing in Conversational AI Tutors for Personalized Learning
Jinwen Tang, Qiming Guo, Zhicheng Tang
https://arxiv.org/abs/2508.11892

RPKT: Learning What You Don't -- Know Recursive Prerequisite Knowledge Tracing in Conversational AI Tutors for Personalized Learning
Educational systems often assume learners can identify their knowledge gaps, yet research consistently shows that students struggle to recognize what they don't know they need to learn-the "unknown unknowns" problem. This paper presents a novel Recursive Prerequisite Knowledge Tracing (RPKT) system that addresses this challenge through dynamic prerequisite discovery using large language models. Unlike existing adaptive learning systems that rely on pre-defined knowledge graphs, our approach rec…

I got a 404media subscription, and you should too. But honestly, there's a lot of stuff that I won't read these days (for my own mental well-being). This is one of those things. The headline is enough, I don't need to know more.
https://hachyderm.io/@evacide/11486976…

"Researchers call this mummy the Younger Lady. DNA analysis indicates that she is King Tut’s mother and a sister of Akhenaten, but her name is unknown."

Gaussian Mixture Model with unknown diagonal covariances via continuous sparse regularization
Romane Giard (ECL, ICJ, PSPM), Yohann de Castro (ICJ, ECL, PSPM, IUF), Cl\'ement Marteau (PSPM, ICJ, UCBL)
https://arxiv.org/abs/2509.12889

Gaussian Mixture Model with unknown diagonal covariances via continuous sparse regularization
This paper addresses the statistical estimation of Gaussian Mixture Models (GMMs) with unknown diagonal covariances from independent and identically distributed samples. We employ the Beurling-LASSO (BLASSO), a convex optimization framework that promotes sparsity in the space of measures, to simultaneously estimate the number of components and their parameters. Our main contribution extends the BLASSO methodology to multivariate GMMs with component-specific unknown diagonal covariance matrices-…

7 drones, projectile fragment of unknown origin found in Poland after Russian incursion: https://benborges.xyz/2025/09/10/drones-projectile-fragment-of-unknown.html

@…
I don't know if you're already aware of this but they have full episodes of all 12 seasons of Welcome to Anthony Bourdain: Parts Unknown on YouTube.
https://www.

Anthony Bourdain Parts Unknown
Welcome to Anthony Bourdain: Parts Unknown YouTube Channel. Watch weekly videos from Chef Anthony Bourdain's travels of the world to discover little-known parts of the world and celebrate diverse cultures by exploring food and culinary rituals. Subscribe to discover all of Anthony Bourdain's adventures around the world: https://www.youtube.com/channel/UCffagYQTThJx4N55UKdr5ew?Sub_confirmation=1

Learning Constraints from Stochastic Partially-Observed Closed-Loop Demonstrations
Chih-Yuan Chiu, Zhouyu Zhang, Glen Chou
https://arxiv.org/abs/2509.15109 https://

Learning Constraints from Stochastic Partially-Observed Closed-Loop Demonstrations
We present an algorithm for learning unknown parametric constraints from locally-optimal input-output trajectory data. We assume that the given data is generated by demonstrators with stochastic nonlinear dynamics who execute a state or output feedback law to robustly satisfy the constraints despite worst-case dynamics and output noise. We encode the Karush-Kuhn-Tucker (KKT) conditions of this robust optimal output feedback control problem within a feasibility problem to recover constraints con…

A Systematic Particle Filter for Estimating Time-Varying Parameters in Advection-Diffusion Equations with Source Terms
Andrea Arnold
https://arxiv.org/abs/2508.12155 https://

A Systematic Particle Filter for Estimating Time-Varying Parameters in Advection-Diffusion Equations with Source Terms
Many real-world systems modeled using partial differential equations (PDEs) involve unknown parameters that must be estimated from limited, noisy system observations. While typically assumed to be constants, some of these unobserved parameters may vary with time. This work proposes a two-phase, offline-online numerical procedure for systematically estimating and quantifying uncertainty in time-varying parameters (TVPs) in time-dependent PDEs, specifically focusing on advection-diffusion models …

Crosslisted article(s) found for cs.GT. https://arxiv.org/list/cs.GT/new
[1/1]:
- Friend or Foe: Delegating to an AI Whose Alignment is Unknown
Drew Fudenberg, Annie Liang

🇺🇦 #NowPlaying on KEXP's #VarietyMix
Stereolab:
🎵 Constant and Uniform Movement Unknown
#Stereolab
#newRelease 🆕 single
https://stereolab.bandcamp.com/album/fed-up-with-your-job-constant-and-uniform-movement-unknown
https://open.spotify.com/track/7mcpINyTYDB9ul7pcRzDMT

Visual artist Brian Jungen on embracing the unknown
#creativity #artist

Visual artist Brian Jungen on embracing the unknown – The Creative Independent
Visual artist Brian Jungen discusses how catastrophe sharpens focus, finding the perfect idea, and the artistic benefits of limitation

🪭 Previously unknown protein 'folding factories' discovered
#biology

Previously unknown protein 'folding factories' discovered
In order to fulfill their many functions, proteins must be folded into the correct shape. Researchers at the University of Basel have now discovered tiny "folding factories" in cells that enable efficient and accurate protein folding. A lack of these structures can lead to diseases such as diabetes and neurodegenerative disorders.

False Data-Injection Attack Detection in Cyber-Physical Systems: A Wasserstein Distributionally Robust Reachability Optimization Approach
Yulin Feng, Dapeng Lan, Chao Shang
https://arxiv.org/abs/2508.12402

False Data-Injection Attack Detection in Cyber-Physical Systems: A Wasserstein Distributionally Robust Reachability Optimization Approach
Cyber-physical system (CPS) is the foundational backbone of modern critical infrastructures, so ensuring its security and resilience against cyber-attacks is of pivotal importance. This paper addresses the challenge of designing anomaly detectors for CPS under false-data injection (FDI) attacks and stochastic disturbances governed by unknown probability distribution. By using the Wasserstein ambiguity set, a prevalent data-driven tool in distributionally robust optimization (DRO), we first prop…

Scheduling on Identical Machines with Setup Time and Unknown Execution Time
Yasushi Kawase, Kazuhisa Makino, Vinh Long Phan, Hanna Sumita
https://arxiv.org/abs/2507.11311

Scheduling on Identical Machines with Setup Time and Unknown Execution Time
In this study, we investigate a scheduling problem on identical machines in which jobs require initial setup before execution. We assume that an algorithm can dynamically form a batch (i.e., a collection of jobs to be processed together) from the remaining jobs. The setup time is modeled as a known monotone function of the set of jobs within a batch, while the execution time of each job remains unknown until completion. This uncertainty poses significant challenges for minimizing the makespan. …

Google says its Big Sleep AI agent for finding unknown software vulnerabilities recently discovered a critical SQLite flaw that "was at risk of being exploited" (Jonathan Greig/The Record)
https://therecord.media/google-big-sleep-ai-tool-found-bug

Google says ‘Big Sleep’ AI tool found bug hackers planned to use
On Tuesday, Google said Big Sleep managed to discover CVE-2025-6965 — a critical security flaw that Google said was “only known to threat actors and was at risk of being exploited.”

"Verrat": Streit zwischen Krafton und ehemaligen "Subnautica"-Managern eskaliert
Der Streit zwischen Krafton und den Gründern des "Subnautica"-Studios Unknown Worlds spitzt sich rasant zu. Auf brisante Vorwürfe folgt offenbar eine Klage.

CogDDN: A Cognitive Demand-Driven Navigation with Decision Optimization and Dual-Process Thinking
Yuehao Huang, Liang Liu, Shuangming Lei, Yukai Ma, Hao Su, Jianbiao Mei, Pengxiang Zhao, Yaqing Gu, Yong Liu, Jiajun Lv
https://arxiv.org/abs/2507.11334

CogDDN: A Cognitive Demand-Driven Navigation with Decision Optimization and Dual-Process Thinking
Mobile robots are increasingly required to navigate and interact within unknown and unstructured environments to meet human demands. Demand-driven navigation (DDN) enables robots to identify and locate objects based on implicit human intent, even when object locations are unknown. However, traditional data-driven DDN methods rely on pre-collected data for model training and decision-making, limiting their generalization capability in unseen scenarios. In this paper, we propose CogDDN, a VLM-bas…

Order Optimal Regret Bounds for Sharpe Ratio Optimization in the Bandit Setting
Mohammad Taha Shah, Sabrina Khurshid, Gourab Ghatak
https://arxiv.org/abs/2508.13749 https://

Order Optimal Regret Bounds for Sharpe Ratio Optimization in the Bandit Setting
In this paper, we investigate the problem of sequential decision-making for Sharpe ratio (SR) maximization in a stochastic bandit setting. We focus on the Thompson Sampling (TS) algorithm, a Bayesian approach celebrated for its empirical performance and exploration efficiency, under the assumption of Gaussian rewards with unknown parameters. Unlike conventional bandit objectives focusing on maximizing cumulative reward, Sharpe ratio optimization instead introduces an inherent tradeoff between a…

GLIDE: A Coordinated Aerial-Ground Framework for Search and Rescue in Unknown Environments
Seth Farrell, Chenghao Li, Hongzhan Yu, Hesam Mojtahedi, Sicun Gao, Henrik I. Christensen
https://arxiv.org/abs/2509.14210

GLIDE: A Coordinated Aerial-Ground Framework for Search and Rescue in Unknown Environments
We present a cooperative aerial-ground search-and-rescue (SAR) framework that pairs two unmanned aerial vehicles (UAVs) with an unmanned ground vehicle (UGV) to achieve rapid victim localization and obstacle-aware navigation in unknown environments. We dub this framework Guided Long-horizon Integrated Drone Escort (GLIDE), highlighting the UGV's reliance on UAV guidance for long-horizon planning. In our framework, a goal-searching UAV executes real-time onboard victim detection and georeferenci…

A Monotonicity-Based Regularization Approach to Shape Reconstruction for the Helmholtz Equation
Sarah Eberle-Blick, Bastian Harrach, Xianchao Wang
https://arxiv.org/abs/2508.11439

A Monotonicity-Based Regularization Approach to Shape Reconstruction for the Helmholtz Equation
We consider an inverse boundary value problem for determining unknown scatterers, which is governed by the Helmholtz equation in a bounded domain. To address this, we develop a novel convex data-fitting formulation that is capable of reconstructing the shape of the unknown scatterers.Our formulation is based on a monotonicity relation between the scattering index and boundary measurements. We use this relation to obtain a pixel-wise constraint on the unknown scattering index, and then minimize …

Efficiently learning depth-3 circuits via quantum agnostic boosting
Srinivasan Arunachalam, Arkopal Dutt, Alexandru Gheorghiu, Michael de Oliveira
https://arxiv.org/abs/2509.14461

Efficiently learning depth-3 circuits via quantum agnostic boosting
We initiate the study of quantum agnostic learning of phase states with respect to a function class $\mathsf{C}\subseteq \{c:\{0,1\}^n\rightarrow \{0,1\}\}$: given copies of an unknown $n$-qubit state $|ψ\rangle$ which has fidelity $\textsf{opt}$ with a phase state $|ϕ_c\rangle=\frac{1}{\sqrt{2^n}}\sum_{x\in \{0,1\}^n}(-1)^{c(x)}|x\rangle$ for some $c\in \mathsf{C}$, output $|ϕ\rangle$ which has fidelity $|\langle ϕ| ψ\rangle|^2 \geq \textsf{opt}-\varepsi…

Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis
Soham Hans, Nikolos Gurney, Stacy Marsella, Sofia Hirschmann
https://arxiv.org/abs/2508.13240 https://

Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis
Understanding and quantifying human cognitive biases from empirical data has long posed a formidable challenge, particularly in cybersecurity, where defending against unknown adversaries is paramount. Traditional cyber defense strategies have largely focused on fortification, while some approaches attempt to anticipate attacker strategies by mapping them to cognitive vulnerabilities, yet they fall short in dynamically interpreting attacks in progress. In recognition of this gap, IARPA's ReSCIND…

Your blinds? What about 'em?

From Who Said What to Who They Are: Modular Training-free Identity-Aware LLM Refinement of Speaker Diarization
Yu-Wen Chen, William Ho, Maxim Topaz, Julia Hirschberg, Zoran Kostic
https://arxiv.org/abs/2509.15082

From Who Said What to Who They Are: Modular Training-free Identity-Aware LLM Refinement of Speaker Diarization
Speaker diarization (SD) struggles in real-world scenarios due to dynamic environments and unknown speaker counts. SD is rarely used alone and is often paired with automatic speech recognition (ASR), but non-modular methods that jointly train on domain-specific data have limited flexibility. Moreover, many applications require true speaker identities rather than SD's pseudo labels. We propose a training-free modular pipeline combining off-the-shelf SD, ASR, and a large language model (LLM) to d…

Possible detection of HFQPOs associated with 'unknown' variability class of GRS 1915 105
Seshadri Majumder (IITG), Santabrata Das (IITG), Anuj Nandi (URSC)
https://arxiv.org/abs/2508.08594

Possible detection of HFQPOs associated with 'unknown' variability class of GRS 1915+105
We present a comprehensive spectro-temporal analysis of GRS $1915+105$ observed with AstroSat during June, $2017$. A detailed study of the temporal properties reveals the appearance of an `unknown' variability class ($τ$) during $ρ\rightarrow κ$ class transition of the source. This new `unknown' class ($τ$) is characterized by the irregular repetition of low count `dips' along with the adjacent `flare' like features in between two successive steady count rate durations, resulting in uniform…

Filing: the Internet Archive and major music labels settle a copyright lawsuit against Internet Archive over its project to preserve early music recordings (Ashley Belanger/Ars Technica)
https://arstechnica.com/tech-policy/20…

Internet Archive’s big battle with music publishers ends in settlement
The true cost of keeping the Internet Archive alive will likely remain unknown.

Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detection
Shun Huang, Zhihua Fang, Liang He
https://arxiv.org/abs/2509.13853 https://

Noise Supervised Contrastive Learning and Feature-Perturbed for Anomalous Sound Detection
Unsupervised anomalous sound detection aims to detect unknown anomalous sounds by training a model using only normal audio data. Despite advancements in self-supervised methods, the issue of frequent false alarms when handling samples of the same type from different machines remains unresolved. This paper introduces a novel training technique called one-stage supervised contrastive learning (OS-SCL), which significantly addresses this problem by perturbing features in the embedding space and em…

Apple’s new mobile operating system, iOS 26, includes a new feature
designed to curb unwanted spam calls and text messages.
It will do so by segregating texts that come from outside a recipient’s contacts into an unknown senders screen,
where they are likely to languish unchecked.
For unknown callers, the phone will automatically respond on users’ behalf
to request more information before asking if they would like to pick up.

How Apple could send democracy to the spam folder
An iPhone update threatens to upend public opinion research.

Secure Blind Graph Signal Recovery and Adversary Detection Using Smoothness Maximization
Mahdi Shamsi, Hadi Zayyani, Hasan Abu Hilal, Mohammad Salman
https://arxiv.org/abs/2509.14449

Secure Blind Graph Signal Recovery and Adversary Detection Using Smoothness Maximization
In this letter, we propose a secure blind Graph Signal Recovery (GSR) algorithm that can detect adversary nodes. Some unknown adversaries are assumed to be injecting false data at their respective nodes in the graph. The number and location of adversaries are not known in advance and the goal is to recover the graph signal in the presence of measurement noise and False Data Injection (FDI) caused by the adversaries. Consequently, the proposed algorithm would be a perfect candidate to solve this…

(Footnote: code that only has to run once is a different beast from code that has to work in a production environement. I’ve noticed a mismatch between data science / research types using LLMs to jump start one-off code and developers writing code for unknown end users / prod env. Serious pitfalls in both cases, but different pitfalls. Understanding that mismatch may help some of these conversation be less unreasonable!)
8/

Varying Horizon Learning Economic MPC With Unknown Costs of Disturbed Nonlinear Systems
Weiliang Xiong, Defeng He, Haiping Du, Jianbin Mu
https://arxiv.org/abs/2509.11823 https:…

Varying Horizon Learning Economic MPC With Unknown Costs of Disturbed Nonlinear Systems
This paper proposes a novel varying horizon economic model predictive control (EMPC) scheme without terminal constraints for constrained nonlinear systems with additive disturbances and unknown economic costs. The general regression learning framework with mixed kernels is first used to reconstruct the unknown cost. Then an online iterative procedure is developed to adjust the horizon adaptively. Again, an elegant horizon-dependent contraction constraint is designed to ensure the convergence of…

Multi-loop spectra in general scalar EFTs and CFTs
Johan Henriksson, Franz Herzog, Stefanos R. Kousvos, Jasper Roosmale Nepveu
https://arxiv.org/abs/2507.12518

Multi-loop spectra in general scalar EFTs and CFTs
We consider the most general effective field theory (EFT) Lagrangian with scalar fields and derivatives, and renormalise it to substantially higher loop order than existing results in the literature. EFT Lagrangians have phenomenological applications, for example by encoding corrections to the Standard Model from unknown new physics. At the same time, scalar EFTs capture the spectrum of Wilson--Fisher conformal field theories (CFTs) in $4-\varepsilon$ dimensions. Our results are enabled by a mo…

Quantitative Stain Mapping in X-ray Virtual Histology
Dominik John, David M. Paganin, Marie-Christine Zdora, Lisa Marie Petzold, Patrick Ilg, Junan Chen, Sara Baggio, Johannes B. Thalhammer, Sami Wirtensohn, Julian Moosmann, J\"org U. Hammel, Felix Beckmann, Samantha J. Alloo, Jannis Ahlers, Madleen Busse, Julia Herzen, Kaye S. Morgan
https://

Quantitative Stain Mapping in X-ray Virtual Histology
Virtual histology is an emerging field in biomedicine that enables three-dimensional tissue visualization using X-ray micro-computed tomography. However, the method still lacks the specificity of conventional histology, in which parts of the tissue are selectively highlighted using targeted stains. Though some first X-ray stains have been developed to address this issue, their precise location and quantity inside the tissue volume remain largely unknown. In this work, we present a novel approac…

ambassador: Philippines Ambassador bombing (2000)
A temporal network representing snapshots of relationships among individuals directly or indirectly associated with Philippines Ambassador Residence Bombing 2000, Jakarta. Nodes are people, and an edge exists if they are associated with each other. Definition of the edge weight is unknown.
This network has 16 nodes and 36 edges.
Tags: Social, Offline, Weighted, Temporal

🇺🇦 #NowPlaying on KEXP's #VarietyMix
MATTERS UNKNOWN:
🎵 I Am the Birds
#MATTERSUNKNOWN
https://mattersunknown.bandcamp.com/track/i-am-the-birds-ft-onipa
https://open.spotify.com/track/2ON15cCpjy31Yauj4pCTsT

https://www.cbc.ca/news/politics/house-of-commons-data-breach-1.7608061
House of Commons hit by cyberattack from 'threat actor': internal email

House of Commons hit by cyberattack from 'threat actor': internal email | CBC News
The House of Commons and Canada’s cybersecurity agency are investigating a significant data breach caused by an unknown “threat actor” targeting employee information.

Cadet opens fire during training in northern Ukraine, killing two: https://benborges.xyz/2025/07/17/cadet-opens-fire-during-training.html

(Exhaustive) Symbolic Regression and model selection by minimum description length
Harry Desmond
https://arxiv.org/abs/2507.13033 https://

(Exhaustive) Symbolic Regression and model selection by minimum description length
Symbolic regression is the machine learning method for learning functions from data. After a brief overview of the symbolic regression landscape, I will describe the two main challenges that traditional algorithms face: they have an unknown (and likely significant) probability of failing to find any given good function, and they suffer from ambiguity and poorly-justified assumptions in their function-selection procedure. To address these I propose an exhaustive search and model selection by the…

🇺🇦 Auf radioeins läuft...
Unknown Mortal Orchestra:
🎵 Nadja
#NowPlaying #UnknownMortalOrchestra
https://unknown-mortal-orchestra.bandcamp.com/track/nadja
https://open.spotify.com/track/2AojQfi1k8pgwyg1Ii0Ly8

A Scalable Formula for the Moments of a Family of Self-Normalized Statistics
Haolin Zou, Heyuan Yao, Victor de la Pe\~na
https://arxiv.org/abs/2509.14428 https://

A Scalable Formula for the Moments of a Family of Self-Normalized Statistics
Following the student t-statistic, normalization has been a widely used method in statistic and other disciplines including economics, ecology and machine learning. We focus on statistics taking the form of a ratio over (some power of) the sample mean, the probabilistic features of which remain unknown. We develop a unified formula for the moments of these self-normalized statistics with non-negative observations, yielding closed-form expressions for several important cases. Moreover, the compl…

@… https://levidayan.substack.com

Unknown Rhythms | Levi Dayan | Substack
words on experimental/improvised/underground music. Click to read Unknown Rhythms, by Levi Dayan, a Substack publication with hundreds of subscribers.

We are not always discovering high mountains - sometimes we also go below earth. 😄
Here we went ~55m below the surface to see what the world below our feet looks like. And yes, the stairs were as steep as they appear on the #photo! The whole walk took us about an hour.
more about it:

My Unknown Truth – The Australian Adoption Podcast
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/my-unknown-truth-the-australian-adoption-podcast/

Evaluation Awareness Scales Predictably in Open-Weights Large Language Models
Maheep Chaudhary, Ian Su, Nikhil Hooda, Nishith Shankar, Julia Tan, Kevin Zhu, Ashwinee Panda, Ryan Lagasse, Vasu Sharma
https://arxiv.org/abs/2509.13333

Evaluation Awareness Scales Predictably in Open-Weights Large Language Models
Large language models (LLMs) can internally distinguish between evaluation and deployment contexts, a behaviour known as \emph{evaluation awareness}. This undermines AI safety evaluations, as models may conceal dangerous capabilities during testing. Prior work demonstrated this in a single $70$B model, but the scaling relationship across model sizes remains unknown. We investigate evaluation awareness across $15$ models scaling from $0.27$B to $70$B parameters from four families using linear pr…

Filing: the Internet Archive and major music labels settle a copyright infringement lawsuit against IA over its project to preserve early music recordings (Ashley Belanger/Ars Technica)
https://arstechnica.com/tech-policy/202…

Internet Archive’s big battle with music publishers ends in settlement
The true cost of keeping the Internet Archive alive will likely remain unknown.

SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning
Zichen Yan, Rui Huang, Lei He, Shao Guo, Lin Zhao
https://arxiv.org/abs/2508.12394 http…

SIGN: Safety-Aware Image-Goal Navigation for Autonomous Drones via Reinforcement Learning
Image-goal navigation (ImageNav) tasks a robot with autonomously exploring an unknown environment and reaching a location that visually matches a given target image. While prior works primarily study ImageNav for ground robots, enabling this capability for autonomous drones is substantially more challenging due to their need for high-frequency feedback control and global localization for stable flight. In this paper, we propose a novel sim-to-real framework that leverages visual reinforcement l…

Adaptive Control with Set-Point Tracking and Linear-like Closed-loop Behavior
Mohamad T. Shahab
https://arxiv.org/abs/2508.12225 https://arxiv.org/pdf/2508…

Adaptive Control with Set-Point Tracking and Linear-like Closed-loop Behavior
In this paper, we consider the problem of set-point tracking for a discrete-time plant with unknown plant parameters belonging to a convex and compact uncertainty set. We carry out parameter estimation for an associated auxiliary plant, and a pole-placement-based control law is employed. We prove that this adaptive controller provides desirable linear-like closed-loop behavior which guarantees a bound consisting of: exponential decay with respect to the initial condition, a linear-like convolut…

Learning to Transmit Over Unknown Erasure Channels with Empirical Erasure Rate Feedback
Haricharan Balasundaram, Krishna Jagannathan
https://arxiv.org/abs/2507.08599

Learning to Transmit Over Unknown Erasure Channels with Empirical Erasure Rate Feedback
We address the problem of reliable data transmission within a finite time horizon $T$ over a binary erasure channel with unknown erasure probability. We consider a feedback model wherein the transmitter can query the receiver infrequently and obtain the empirical erasure rate experienced by the latter. We aim to minimize a regret quantity, i.e. how much worse a strategy performs compared to an oracle who knows the probability of erasure, while operating at the same block error rate. A learning …

Covering Unknown Correlations in Bayesian Priors by Inflating Uncertainties
Lukas Koch
https://arxiv.org/abs/2509.11821 https://arxiv.org/pdf/2509.11821

Covering Unknown Correlations in Bayesian Priors by Inflating Uncertainties
Bayesian analyses require that all variable model parameters are given a prior probability distribution. This can pose a challenge for analyses where multiple experiments are combined if these experiments use different parametrisations for their nuisance parameters. If the parameters in the two models describe exactly the same physics, they should be 100% correlated in the prior. If the parameters describe independent physics, they should be uncorrelated. But if they describe related or overlap…

Musician YATTA on going towards the unknown https://thecreativeindependent.com/people/musician-yatta-on-going-towards-…

Musician YATTA on going towards the unknown – The Creative Independent
Musician YATTA (aka ricky sallay zoker) discusses not playing shows after an album release, following their curiosity, and seeking slow and supportive environments to see the work through.

🇺🇦 #NowPlaying on #KEXP's #DriveTime
Stereolab:
🎵 Constant and Uniform Movement Unknown
#Stereolab
#newRelease 🆕 single
https://stereolab.bandcamp.com/album/fed-up-with-your-job-constant-and-uniform-movement-unknown
https://open.spotify.com/track/7mcpINyTYDB9ul7pcRzDMT

Sample-Constrained Black Box Optimization for Audio Personalization
Rajalaxmi Rajagopalan, Yu-Lin Wei, Romit Roy Choudhury
https://arxiv.org/abs/2507.12773

Sample-Constrained Black Box Optimization for Audio Personalization
We consider the problem of personalizing audio to maximize user experience. Briefly, we aim to find a filter $h^*$, which applied to any music or speech, will maximize the user's satisfaction. This is a black-box optimization problem since the user's satisfaction function is unknown. Substantive work has been done on this topic where the key idea is to play audio samples to the user, each shaped by a different filter $h_i$, and query the user for their satisfaction scores $f(h_i)$. A family of …

Towards multi-purpose locally differentially-private synthetic data release via spline wavelet plug-in estimation
Thibault Randrianarisoa, Lukas Steinberger, Botond Szab\'o
https://arxiv.org/abs/2508.13969

Towards multi-purpose locally differentially-private synthetic data release via spline wavelet plug-in estimation
We develop plug-in estimators for locally differentially private semi-parametric estimation via spline wavelets. The approach leads to optimal rates of convergence for a large class of estimation problems that are characterized by (differentiable) functionals $Λ(f)$ of the true data generating density $f$. The crucial feature of the locally private data $Z_1,\dots, Z_n$ we generate is that it does not depend on the particular functional $Λ$ (or the unknown density $f$) the analyst wants to es…

Scalable Sensor Placement for Cyclic Networks with Observability Guarantees: Application to Water Distribution Networks
J. J. H. van Gemert, V. Breschi, D. R. Yntema, K. J. Keesman, M. Lazar
https://arxiv.org/abs/2508.13604

Scalable Sensor Placement for Cyclic Networks with Observability Guarantees: Application to Water Distribution Networks
Optimal sensor placement is essential for state estimation and effective network monitoring. As known in the literature, this problem becomes particularly challenging in large-scale undirected or bidirected cyclic networks with parametric uncertainties, such as water distribution networks (WDNs), where pipe resistance and demand patterns are often unknown. Motivated by the challenges of cycles, parametric uncertainties, and scalability, this paper proposes a sensor placement algorithm that guar…

chicago_road: Chicago road network
A transportation network of Chicago, USA, from an unknown date (probably late 20th century). Nodes represent intersections or endpoints and edge represent the road connecting these nodes.
This network has 12982 nodes and 39018 edges.
Tags: Transportation, Roads, Unweighted
https://

Trump is so open for (corrupt) business that he’s potentially profiting millions
from a deal with a mysterious crypto company
whose owners and intentions are essentially unknown, at least to those outside Trump’s circle.
However, as is the norm under this administration, the losers are crystal clear: the American people.

Week 24: President Trump Notches Another Mysterious Benefactor
A mysterious investor is the latest entrant into the Trump crypto grift, and more stories from the MAGAswamp.

Learning to Detect Unknown Jailbreak Attacks in Large Vision-Language Models: A Unified and Accurate Approach
Shuang Liang, Zhihao Xu, Jialing Tao, Hui Xue, Xiting Wang
https://arxiv.org/abs/2508.09201

Learning to Detect Unknown Jailbreak Attacks in Large Vision-Language Models: A Unified and Accurate Approach
Despite extensive alignment efforts, Large Vision-Language Models (LVLMs) remain vulnerable to jailbreak attacks, posing serious safety risks. Although recent detection works have shifted to internal representations due to their rich cross-modal information, most methods rely on heuristic rules rather than principled objectives, resulting in suboptimal performance. To address these limitations, we propose Learning to Detect (LoD), a novel unsupervised framework that formulates jailbreak detecti…

Near-perfect Clustering Based on Recursive Binary Splitting Using Max-MMD
Sourav Chakrabarty, Anirvan Chakraborty, Shyamal K. De
https://arxiv.org/abs/2507.11158

Near-perfect Clustering Based on Recursive Binary Splitting Using Max-MMD
We develop novel clustering algorithms for functional data when the number of clusters $K$ is unknown and also when it is prefixed. These algorithms are developed based on the Maximum Mean Discrepancy (MMD) measure between two sets of observations. The algorithms recursively use a binary splitting strategy to partition the dataset into two subgroups such that they are maximally separated in terms of an appropriate weighted MMD measure. When $K$ is unknown, the proposed clustering algorithm has …

Beyond Diagonal Reconfigurable Intelligent Surface Enabled Sensing: Cramer-Rao Bound Optimization
Xiaoqi Zhang, Liang Liu, Shuowen Zhang, Haijun Zhang
https://arxiv.org/abs/2508.11292

Beyond Diagonal Reconfigurable Intelligent Surface Enabled Sensing: Cramer-Rao Bound Optimization
Recently, beyond diagonal reconfigurable intelligent surface (BD-RIS) has emerged as a more flexible solution to engineer the wireless propagation channels, thanks to its non-diagonal reflecting matrix. Although the gain of the BD-RIS over the conventional RIS in communication has been revealed in many works, its gain in 6G sensing is still unknown. This motivates us to study the BD-RIS assisted sensing in this letter. Specifically, we derive the Cramer-Rao bound (CRB) for estimating the angle-…

PaiP: An Operational Aware Interactive Planner for Unknown Cabinet Environments
Chengjin Wang, Zheng Yan, Yanmin Zhou, Runjie Shen, Zhipeng Wang, Bin Cheng, Bin He
https://arxiv.org/abs/2509.11516

PaiP: An Operational Aware Interactive Planner for Unknown Cabinet Environments
Box/cabinet scenarios with stacked objects pose significant challenges for robotic motion due to visual occlusions and constrained free space. Traditional collision-free trajectory planning methods often fail when no collision-free paths exist, and may even lead to catastrophic collisions caused by invisible objects. To overcome these challenges, we propose an operational aware interactive motion planner (PaiP) a real-time closed-loop planning framework utilizing multimodal tactile perception. …

chicago_road: Chicago road network
A transportation network of Chicago, USA, from an unknown date (probably late 20th century). Nodes represent intersections or endpoints and edge represent the road connecting these nodes.
This network has 12982 nodes and 39018 edges.
Tags: Transportation, Roads, Unweighted
https://

🇺🇦 #NowPlaying on KEXP's #DriveTime
Unknown Mortal Orchestra:
🎵 ONE HUNDRED BATS
#UnknownMortalOrchestra
https://unknown-mortal-orchestra.bandcamp.com/track/one-hundred-bats
https://open.spotify.com/track/5hdrbks3ryBnWAtYXSlMdf

From Formal Methods to Data-Driven Safety Certificates of Unknown Large-Scale Networks
Omid Akbarzadeh, Behrad Samari, Amy Nejati, Abolfazl Lavaei
https://arxiv.org/abs/2508.09520

From Formal Methods to Data-Driven Safety Certificates of Unknown Large-Scale Networks
In this work, we propose a data-driven scheme within a compositional framework with noisy data to design robust safety controllers in a fully decentralized fashion for large-scale interconnected networks with unknown mathematical dynamics. Despite the network's high dimensionality and the inherent complexity of its unknown model, which make it intractable, our approach effectively addresses these challenges by (i) treating the network as a composition of smaller subsystems, and (ii) collecting …

Extracting and charging energy into almost unknown quantum states
Andrea Canzio, Vasco Cavina, Roberto Menta, Vittorio Giovannetti
https://arxiv.org/abs/2509.08899 https://

Extracting and charging energy into almost unknown quantum states
In this work, we investigate the amount of energy that can be extracted or charged through unitary operations when only minimal information about the state is known. Assuming knowledge of only the mean energy of the state, we start by developing optimal upper bounds for the work that can be unitarily extracted or charged in this scenario. In deriving these upper bounds, we provide a complete characterization of the minimum ergotropy and anti-ergotropy for density matrices with fixed average ene…

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
Giulio Giacomuzzo, Mohamed Abdelwahab, Marco Cal\`i, Alberto Dalla Libera, Ruggero Carli
https://arxiv.org/abs/2507.11170

A Robust Controller based on Gaussian Processes for Robotic Manipulators with Unknown Uncertainty
In this paper, we propose a novel learning-based robust feedback linearization strategy to ensure precise trajectory tracking for an important family of Lagrangian systems. We assume a nominal knowledge of the dynamics is given but no a-priori bounds on the model mismatch are available. In our approach, the key ingredient is the adoption of a regression framework based on Gaussian Processes (GPR) to estimate the model mismatch. This estimate is added to the outer loop of a classical feedback li…

Minimum-norm interpolation for unknown surface reconstruction
Alex Shiu Lun Chu, Leevan Ling, Ka Chun Cheung
https://arxiv.org/abs/2507.08632 https://

Minimum-norm interpolation for unknown surface reconstruction
We study algorithms to estimate geometric properties of raw point cloud data through implicit surface representations. Given that any level-set function with a constant level set corresponding to the surface can be used for such estimations, numerical methods need not specify a unique target function for the domain-type interpolation problems. In this paper, we focus on kernel-based interpolation by radial basis functions (RBF) and reformulate the uniquely solvable interpolation problem into a …

Data-Driven Safety Certificates of Infinite Networks with Unknown Models and Interconnection Topologies
Mahdieh Zaker, Amy Nejati, Abolfazl Lavaei
https://arxiv.org/abs/2507.10979

Data-Driven Safety Certificates of Infinite Networks with Unknown Models and Interconnection Topologies
Infinite networks are complex interconnected systems comprising a countably infinite number of subsystems, where counting them precisely poses a significant challenge due to the seemingly endless interconnected nature of the network (e.g., counting vehicles on the road). In such scenarios, the presence of infinitely many subsystems within the network renders the existing analysis frameworks tailored for finite networks inapplicable to infinite ones. This paper is concerned with offering a data-…

ambassador: Philippines Ambassador bombing (2000)
A temporal network representing snapshots of relationships among individuals directly or indirectly associated with Philippines Ambassador Residence Bombing 2000, Jakarta. Nodes are people, and an edge exists if they are associated with each other. Definition of the edge weight is unknown.
This network has 16 nodes and 1 edges.
Tags: Social, Offline, Weighted, Temporal

Transmitter Subspace-Aware Target Detection in Two-Channel Passive Radars with Inter-Receiver Collaboration
Nandan Sriranga, Haodong Yang, Pramod K. Varshney
https://arxiv.org/abs/2509.13287

Transmitter Subspace-Aware Target Detection in Two-Channel Passive Radars with Inter-Receiver Collaboration
We address target detection in a single Delay-Doppler cell using spatially distributed two-channel passive radars. An unknown illuminator of opportunity (IO) is assumed to emit a waveform lying in a known low-dimensional subspace (e.g., OFDM). Each receiver transforms its reference and surveillance signals onto the IO subspace after noise-whitening, to obtain cross-correlation (CC) measurements. To save bandwidth, receivers collaboratively exchange and linearly combine the CC output, and only a…

Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data
Sanjeev Ramkumar Sudha, Joel Jose, Erlend M. Coates
https://arxiv.org/abs/2507.13053

Efficient Online Learning and Adaptive Planning for Robotic Information Gathering Based on Streaming Data
Robotic information gathering (RIG) techniques refer to methods where mobile robots are used to acquire data about the physical environment with a suite of sensors. Informative planning is an important part of RIG where the goal is to find sequences of actions or paths that maximize efficiency or the quality of information collected. Many existing solutions solve this problem by assuming that the environment is known in advance. However, real environments could be unknown or time-varying, and a…

Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel
Fidel Aniano Causil Barrios, Alex Rodrigo dos Santos Sousa
https://arxiv.org/abs/2507.11718

Bayesian wavelet shrinkage for low SNR data based on the Epanechnikov kernel
Consider the univariate nonparametric regression model with additive Gaussian noise and the representation of the unknown regression function in terms of a wavelet basis. We propose a shrinkage rule to estimate the wavelet coefficients obtained by mixing a point mass function at zero with the Epanechnikov distribution as a prior for the coefficients. The proposed rule proved to be suitable for application in scenarios with low signal-to-noise ratio datasets and outperformed standard and Bayesia…

On the Fundamental Resource for Exponential Advantage in Quantum Channel Learning
Minsoo Kim, Changhun Oh
https://arxiv.org/abs/2507.11089 https://

On the Fundamental Resource for Exponential Advantage in Quantum Channel Learning
Quantum resources enable us to achieve an exponential advantage in learning the properties of unknown physical systems by employing quantum memory. While entanglement with quantum memory is recognized as a necessary qualitative resource, its quantitative role remains less understood. In this work, we distinguish between two fundamental resources provided by quantum memory -- entanglement and ancilla qubits -- and analyze their separate contributions to the sampling complexity of quantum learnin…

Bridging Centralized and Distributed Frameworks in Unknown Input Observer Design
Ruixuan Zhao, Guitao Yang, Peng Li, Boli Chen
https://arxiv.org/abs/2509.08914 https://

Bridging Centralized and Distributed Frameworks in Unknown Input Observer Design
State estimation for linear time-invariant systems with unknown inputs is a fundamental problem in various research domains. In this article, we establish conditions for the design of unknown input observers (UIOs) from a geometric approach perspective. Specifically, we derive a necessary and sufficient geometric condition for the existence of a centralized UIO. Compared to existing results, our condition offers a more general design framework, allowing designers the flexibility to estimate par…

wordnet: WordNet relationships
A network of English words from the WordNet. Node is a word, and edge denotes relationships between words (synonymy, hyperonymy, meronymy, etc.). The date at which this network was extracted from WordNet is not unknown.
This network has 146005 nodes and 656999 edges.
Tags: Informational, Language, Unweighted
htt…

Beyond Robustness: Learning Unknown Dynamic Load Adaptation for Quadruped Locomotion on Rough Terrain
Leixin Chang, Yuxuan Nai, Hua Chen, Liangjing Yang
https://arxiv.org/abs/2507.07825

Beyond Robustness: Learning Unknown Dynamic Load Adaptation for Quadruped Locomotion on Rough Terrain
Unknown dynamic load carrying is one important practical application for quadruped robots. Such a problem is non-trivial, posing three major challenges in quadruped locomotion control. First, how to model or represent the dynamics of the load in a generic manner. Second, how to make the robot capture the dynamics without any external sensing. Third, how to enable the robot to interact with load handling the mutual effect and stabilizing the load. In this work, we propose a general load modeling…

Online Learning and Coverage of Unknown Fields Using Random-Feature Gaussian Processes
Ruijie Du, Ruoyu Lin, Yanning Shen, Magnus Egerstedt
https://arxiv.org/abs/2509.08117 http…

Online Learning and Coverage of Unknown Fields Using Random-Feature Gaussian Processes
This paper proposes a framework for multi-robot systems to perform simultaneous learning and coverage of the domain of interest characterized by an unknown and potentially time-varying density function. To overcome the limitations of Gaussian Process (GP) regression, we employ Random Feature GP (RFGP) and its online variant (O-RFGP) that enables online and incremental inference. By integrating these with Voronoi-based coverage control and Upper Confidence Bound (UCB) sampling strategy, a team o…

A Multi-Level Similarity Approach for Single-View Object Grasping: Matching, Planning, and Fine-Tuning
Hao Chen, Takuya Kiyokawa, Zhengtao Hu, Weiwei Wan, Kensuke Harada
https://arxiv.org/abs/2507.11938

A Multi-Level Similarity Approach for Single-View Object Grasping: Matching, Planning, and Fine-Tuning
Grasping unknown objects from a single view has remained a challenging topic in robotics due to the uncertainty of partial observation. Recent advances in large-scale models have led to benchmark solutions such as GraspNet-1Billion. However, such learning-based approaches still face a critical limitation in performance robustness for their sensitivity to sensing noise and environmental changes. To address this bottleneck in achieving highly generalized grasping, we abandon the traditional learn…

Distributed Unknown Input Observer Design with Relaxed Conditions: Theory and Application to Vehicle Platooning
Ruixuan Zhao, Guitao Yang, Thomas Parisini, Boli Chen
https://arxiv.org/abs/2509.08783

Distributed Unknown Input Observer Design with Relaxed Conditions: Theory and Application to Vehicle Platooning
Designing observers for linear systems with both known and unknown inputs is an important problem in several research contexts, for example, fault diagnosis and fault-tolerant control, and cyber-secure control systems, and presents significant challenges in distributed state estimation due to the limited sensing capabilities of individual nodes. Existing methods typically impose an individual input-to-output rank condition on each estimator node, which severely restricts applicability in practi…

Regrasp Maps for Sequential Manipulation Planning
Svetlana Levit, Marc Toussaint
https://arxiv.org/abs/2507.12407 https://arxiv.org/p…

Regrasp Maps for Sequential Manipulation Planning
We consider manipulation problems in constrained and cluttered settings, which require several regrasps at unknown locations. We propose to inform an optimization-based task and motion planning (TAMP) solver with possible regrasp areas and grasp sequences to speed up the search. Our main idea is to use a state space abstraction, a regrasp map, capturing the combinations of available grasps in different parts of the configuration space, and allowing us to provide the solver with guesses for the …

PILOC: A Pheromone Inverse Guidance Mechanism and Local-Communication Framework for Dynamic Target Search of Multi-Agent in Unknown Environments
Hengrui Liu, Yi Feng, Qilong Zhang
https://arxiv.org/abs/2507.07376

PILOC: A Pheromone Inverse Guidance Mechanism and Local-Communication Framework for Dynamic Target Search of Multi-Agent in Unknown Environments
Multi-Agent Search and Rescue (MASAR) plays a vital role in disaster response, exploration, and reconnaissance. However, dynamic and unknown environments pose significant challenges due to target unpredictability and environmental uncertainty. To tackle these issues, we propose PILOC, a framework that operates without global prior knowledge, leveraging local perception and communication. It introduces a pheromone inverse guidance mechanism to enable efficient coordination and dynamic target loc…

GRATE: a Graph transformer-based deep Reinforcement learning Approach for Time-efficient autonomous robot Exploration
Haozhan Ni, Jingsong Liang, Chenyu He, Yuhong Cao, Guillaume Sartoretti
https://arxiv.org/abs/2509.12863

GRATE: a Graph transformer-based deep Reinforcement learning Approach for Time-efficient autonomous robot Exploration
Autonomous robot exploration (ARE) is the process of a robot autonomously navigating and mapping an unknown environment. Recent Reinforcement Learning (RL)-based approaches typically formulate ARE as a sequential decision-making problem defined on a collision-free informative graph. However, these methods often demonstrate limited reasoning ability over graph-structured data. Moreover, due to the insufficient consideration of robot motion, the resulting RL policies are generally optimized to mi…