Tootfinder

So #today I adjusted my heat-pump space heat weather-compensation schedule, ready for December, as one does...
https://www.earth.org.uk/heat-pump-16WW-control.html#2025-07-06

16WW Heat-pump Control
Settings and control of the heat pump at 16WW. #heatPump #controls #DHW #spaceHeat

Don't leave for the weekend without reading today's Metacurity for the top infosec developments you should know, including
--US feds seize nearly $8 million from fake DPRK IT workers
--Cellebrite buys Corellium for $200m,
--Play gang breached 900 orgs,
--Two ViLE members sentenced,
--UK revenue offices lost $64m to phishing scam,
--One-fifth of Ukraine IP space under Russian control,
--Cairncross defends his lack of tech expertise,
--Ukr…

US feds seize nearly $8 million from fake DPRK IT workers
Cellebrite buys Corellium for $200m, Play gang breached 900 orgs, Two ViLE members sentenced, UK revenue offices lost $64m to phishing scam, One-fifth of Ukraine IP space under Russian control, Cairncross defends his lack of tech expertise, Ukraine claims hack of Russian aviation giant, much more

Maximizing the Value of Predictions in Control: Accuracy Is Not Enough
Yiheng Lin, Christopher Yeh, Zaiwei Chen, Adam Wierman
https://arxiv.org/abs/2506.04497

Maximizing the Value of Predictions in Control: Accuracy Is Not Enough
We study the value of stochastic predictions in online optimal control with random disturbances. Prior work provides performance guarantees based on prediction error but ignores the stochastic dependence between predictions and disturbances. We introduce a general framework modeling their joint distribution and define "prediction power" as the control cost improvement from the optimal use of predictions compared to ignoring the predictions. In the time-varying Linear Quadratic Regulator (LQR) s…

from my link log —
Recovering control flow structures from Java bytecode without CFGs.
https://purplesyringa.moe/blog/recovering-control-flow-structures-without-cfgs/
saved 2025-06-06

Recovering control flow structures without CFGs
I’m working on a Java decompiler because I’m not satisfied with the performance of other solutions. I’ve always heard that decompiling JVM bytecode is a solved problem, but I’ve concluded that the decompilation methods used by CFR and Vineflower are hacky, inefficient, and sometimes don’t even work. The existing solutions are haphazard and inadequate compared to alternative approaches. Specifically, I have beef with the control flow extraction strategies employed by most decompilers. …

SECNEURON: Reliable and Flexible Abuse Control in Local LLMs via Hybrid Neuron Encryption
Zhiqiang Wang, Haohua Du, Junyang Wang, Haifeng Sun, Kaiwen Guo, Haikuo Yu, Chao Liu, Xiang-Yang Li
https://arxiv.org/abs/2506.05242

SECNEURON: Reliable and Flexible Abuse Control in Local LLMs via Hybrid Neuron Encryption
Large language models (LLMs) with diverse capabilities are increasingly being deployed in local environments, presenting significant security and controllability challenges. These locally deployed LLMs operate outside the direct control of developers, rendering them more susceptible to abuse. Existing mitigation techniques mainly designed for cloud-based LLM services are frequently circumvented or ineffective in deployer-controlled environments. We propose SECNEURON, the first framework that se…

This https://arxiv.org/abs/2502.03278 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Fault-Tolerant Control for System Availability and Continuous Operation in Heavy-Duty Wheeled Mobile Robots
When the control system in a heavy-duty wheeled mobile robot (HD-WMR) malfunctions, deviations from ideal motion occur, significantly heightening the risks of off-road instability and costly damage. To meet the demands for safety, reliability, and controllability in HD-WMRs, the control system must tolerate faults to a certain extent, ensuring continuous operation. To this end, this paper introduces a model-free hierarchical control with fault accommodation (MFHCA) framework designed to address…

Series D, Episode 09 - Sand
AVON: Let's get going.
REEVE: Fine landing, Chasgo. You certainly put the lady out of business. [To Servalan] If there is anyone on this planet, you'll have to leave them to me now.
https://blake.torpidity.net/m/409/37 B7B5

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

MISC: Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints
Shivam Chaubey, Francesco Verdoja, Shankar Deka, Ville Kyrki
https://arxiv.org/abs/2507.02438

MISC: Minimal Intervention Shared Control with Guaranteed Safety under Non-Convex Constraints
Shared control combines human intention with autonomous decision-making, from low-level safety overrides to high-level task guidance, enabling systems that adapt to users while ensuring safety and performance. This enhances task effectiveness and user experience across domains such as assistive robotics, teleoperation, and autonomous driving. However, existing shared control methods, based on e.g. Model Predictive Control, Control Barrier Functions, or learning-based control, struggle with feas…

A Framework Leveraging Large Language Models for Autonomous UAV Control in Flying Networks
Diana Nunes, Ricardo Amorim, Pedro Ribeiro, Andr\'e Coelho, Rui Campos
https://arxiv.org/abs/2506.04404

A Framework Leveraging Large Language Models for Autonomous UAV Control in Flying Networks
This paper proposes FLUC, a modular framework that integrates open-source Large Language Models (LLMs) with Unmanned Aerial Vehicle (UAV) autopilot systems to enable autonomous control in Flying Networks (FNs). FLUC translates high-level natural language commands into executable UAV mission code, bridging the gap between operator intent and UAV behaviour. FLUC is evaluated using three open-source LLMs - Qwen 2.5, Gemma 2, and LLaMA 3.2 - across scenarios involving code generation and mission …

CamCloneMaster: Enabling Reference-based Camera Control for Video Generation
Yawen Luo, Jianhong Bai, Xiaoyu Shi, Menghan Xia, Xintao Wang, Pengfei Wan, Di Zhang, Kun Gai, Tianfan Xue
https://arxiv.org/abs/2506.03140

CamCloneMaster: Enabling Reference-based Camera Control for Video Generation
Camera control is crucial for generating expressive and cinematic videos. Existing methods rely on explicit sequences of camera parameters as control conditions, which can be cumbersome for users to construct, particularly for intricate camera movements. To provide a more intuitive camera control method, we propose CamCloneMaster, a framework that enables users to replicate camera movements from reference videos without requiring camera parameters or test-time fine-tuning. CamCloneMaster seamle…

🇺🇦 #NowPlaying on KEXP's #DriveTime
The Clash:
🎵 Complete Control
#TheClash
https://suicidekingrecords.bandcamp.com/track/complete-control-the-clash
https://open.spotify.com/track/0mlwPYU0ApElSjZN4yA4L9

Access Control Threatened by Quantum Entanglement
Zhicheng Zhang, Mingsheng Ying
https://arxiv.org/abs/2507.02622 https://arxiv.org/p…

Access Control Threatened by Quantum Entanglement
Access control is a cornerstone of computer security that prevents unauthorised access to resources. In this paper, we study access control in quantum computer systems. We present the first explicit scenario of a security breach when a classically secure access control system is straightforwardly adapted to the quantum setting. The breach is ultimately due to that quantum mechanics allows the phenomenon of entanglement and violates Mermin inequality, a multi-party variant of the celebrated Bell…

This https://arxiv.org/abs/2505.19887 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Deconstructing Obfuscation: A four-dimensional framework for evaluating Large Language Models assembly code deobfuscation capabilities
Large language models (LLMs) have shown promise in software engineering, yet their effectiveness for binary analysis remains unexplored. We present the first comprehensive evaluation of commercial LLMs for assembly code deobfuscation. Testing seven state-of-the-art models against four obfuscation scenarios (bogus control flow, instruction substitution, control flow flattening, and their combination), we found striking performance variations--from autonomous deobfuscation to complete failure. We…

Control-R: Towards controllable test-time scaling
Di Zhang, Weida Wang, Junxian Li, Xunzhi Wang, Jiatong Li, Jianbo Wu, Jingdi Lei, Haonan He, Peng Ye, Shufei Zhang, Wanli Ouyang, Yuqiang Li, Dongzhan Zhou
https://arxiv.org/abs/2506.00189

Control-R: Towards controllable test-time scaling
This paper target in addressing the challenges of underthinking and overthinking in long chain-of-thought (CoT) reasoning for Large Reasoning Models (LRMs) by introducing Reasoning Control Fields (RCF)--a novel test-time approach that injects structured control signals to guide reasoning from a tree search perspective. RCF enables models to adjust reasoning effort according to given control conditions when solving complex tasks. Additionally, we present the Control-R-4K dataset, which consists …

Republicans just cut Medicaid. Will it cost them control of Congress? (Politico)
https://www.politico.com/news/2025/07/06/republican-medicaid-cuts-midterms-big-beautiful-bill-00440023
http://www.memeorandum.com/250706/p40#a250706p40

Was Residual Penalty and Neural Operators All We Needed for Solving Optimal Control Problems?
Oliver G. S. Lundqvist, Fabricio Oliveira
https://arxiv.org/abs/2506.04742

Was Residual Penalty and Neural Operators All We Needed for Solving Optimal Control Problems?
Neural networks have been used to solve optimal control problems, typically by training neural networks using a combined loss function that considers data, differential equation residuals, and objective costs. We show that including cost functions in the training process is unnecessary, advocating for a simpler architecture and streamlined approach by decoupling the optimal control problem from the training process. Thus, our work shows that a simple neural operator architecture, such as DeepON…

@… Yes, compose with existing tech!
GNU Parallel and Make do good job control.

Control How Your Content Is Used for AI Training With Cloudflare (Cloudflare Blog, 1 July 2024)
#MediaLit

Control content use for AI training with Cloudflare’s managed robots.txt and blocking for monetized content
Cloudflare is making it easier for publishers and content creators of all sizes to prevent their content from being scraped for AI training by managing robots.txt on their behalf, and allowing targeted blocking of AI crawling on sites that serve ads.

Why I Don’t Use #Mocking Frameworks and Why You Might Not Need Them Either by @…

@… this is really interesting: https://bonfirenetworks.org/

Sources: Apple may announce new features for AirPods at WWDC, including iPhone camera control, new head gestures, and sleep detection for pausing playback (Marcus Mendes/9to5Mac)
https://9to5mac.com/2025/06/04/exclusive-airpods-ios-26-wwdc-features/…

Exclusive: AirPods to get camera control, sleep detection, new gestures - 9to5Mac
Today, we are sharing a five previously unreported AirPods features that may be announced as early as Monday, during the WWDC25 keynote.

ROSGuard: A Bandwidth Regulation Mechanism for ROS2-based Applications
Jon Altonaga Puente, Enrico Mezzetti, Irune Agirre Troncoso, Jaume Abella Ferrer, Francisco J. Cazorla Almeida
https://arxiv.org/abs/2506.04640

ROSGuard: A Bandwidth Regulation Mechanism for ROS2-based Applications
Multicore timing interference, arising when multiple requests contend for the same shared hardware resources, is a primary concern for timing verification and validation of time-critical applications. Bandwidth control and regulation approaches have been proposed in the literature as an effective method to monitor and limit the impact of timing interference at run time. These approaches seek for fine-grained control of the bandwidth consumption (at the microsecond level) to meet stringent timin…

Low noise flux estimate and data quality control monitoring in EUCLID-NISP cosmological survey
B. Kubik, R. Barbier, P. Calabria, A. Castera, E. Chabanat, F. Charlieu, J-C. Clemens, A. Ealet, S. Ferriol, W. Gillard, T. Maciaszek, E. Prieto, F. Schirra, A. Secroun, B. Serra, G. Smadja, A. Tilquin, J. Zoubianb
https://arxiv.org/ab…

Low noise flux estimate and data quality control monitoring in EUCLID-NISP cosmological survey
Euclid mission is designed to understand the dark sector of the universe. Precise redshift measurements are provided by H2RG detectors. We propose an unbiased method of fitting the flux with Poisson distributed and correlated data, which has an analytic solution and provides a reliable quality factor - fundamental features to ensure the goals of the mission. We compare our method to other techniques of signal estimation and illustrate the anomaly detection on the flight like detectors. Although…

Information-Optimal Sensing and Control in High-Intensity Laser Experiments
A. D\"opp, C. Eberle, J. Esslinger, S. Howard, F. Irshad, J. Schroeder, N. Weisse, S. Karsch
https://arxiv.org/abs/2506.04946

Information-Optimal Sensing and Control in High-Intensity Laser Experiments
High-intensity laser systems present unique measurement and optimization challenges due to their high complexity, low repetition rates, and shot-to-shot variations. We discuss recent developments towards a unified framework based on information theory and Bayesian inference that addresses these challenges. Starting from fundamental constraints on the physical field structure, we recently demonstrated how to capture complete spatio-temporal information about individual petawatt laser pulses. Bui…

Meine Logitec-BT-Lautsprecher mal wieder aktiviert und bisschen mitm EQ rumgetjuhnt.
„Murder“ von Faderhead boomt ganz gut. ^^

My favorite kind of #tankies, are the #Juche tankies, because nothing says ‘freedom’ like worshipping a dictatorship that starves its own people while pretending self-reliance means total control.

Je viens de poster : « Would it be possible to implement remote web control of SMS messages in #FossifyMessage, like https://messages.google.com/? »

Control Signaling for Reconfigurable Intelligent Surfaces: How Many Bits are Needed?
Anders Enqvist, \"Ozlem Tu\u{g}fe Demir, Cicek Cavdar, Emil Bj\"ornson
https://arxiv.org/abs/2506.03929

Control Signaling for Reconfigurable Intelligent Surfaces: How Many Bits are Needed?
Reconfigurable intelligent surfaces (RISs) can greatly improve the signal quality of future communication systems by reflecting transmitted signals toward the receiver. However, even when the base station (BS) has perfect channel knowledge and can compute the optimal RIS phase-shift configuration, implementing this configuration requires feedback signaling over a control channel from the BS to the RIS. This feedback must be kept minimal, as it is transmitted wirelessly every time the channel ch…

Experimental evaluation of advanced control strategies for high-blockage cross-flow turbine arrays
Aidan Hunt, Gregory Talpey, Brian Polagye
https://arxiv.org/abs/2507.02194

Experimental evaluation of advanced control strategies for high-blockage cross-flow turbine arrays
In river or tidal channels, cross-flow turbines can achieve higher blockage ratios than other turbine variants, and are therefore able to achieve higher efficiencies. Here, we experimentally investigate how array control strategies might further influence the efficiency of a high-blockage dual-rotor cross-flow turbine array. Array performance is evaluated under coordinated constant speed control, uncoordinated torque control, and coordinated intracycle speed control at blockage ratios of 35% - …

Predictive Control over LAWN: Joint Trajectory Design and Resource Allocation
Haijia Jin, Jun Wu, Weijie Yuan, Ruizhi Ruan, Jiacheng Wang, Dusit Niyato, Dong In Kim, Abbas Jamalipour
https://arxiv.org/abs/2507.02374

Predictive Control over LAWN: Joint Trajectory Design and Resource Allocation
Low-altitude wireless networks (LAWNs) have been envisioned as flexible and transformative platforms for enabling delay-sensitive control applications in Internet of Things (IoT) systems. In this work, we investigate the real-time wireless control over a LAWN system, where an aerial drone is employed to serve multiple mobile automated guided vehicles (AGVs) via finite blocklength (FBL) transmission. Toward this end, we adopt the model predictive control (MPC) to ensure accurate trajectory track…

Switzerland was invented by the king of Sweden to control global financial systems using mind control through the audio frequency of yodeling ☝️#SwitzerlandIsFake

The portable air conditioner unit we have in the bedroom is reasonably advanced: it has WiFi connectivity, a remote control, and the ability to switch between heating and cooling. It's annoying, though, that you can only set a sleep timer on the unit itself, which isn't great when you're all nice and comfy in bed. It also takes several button presses when you're there. Fixed thanks to #HomeAssistant, meaning it can be set through the app or voice 😄

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Zilin Kang, Chenyuan Hu, Yu Luo, Zhecheng Yuan, Ruijie Zheng, Huazhe Xu
https://arxiv.org/abs/2507.02712

A Forget-and-Grow Strategy for Deep Reinforcement Learning Scaling in Continuous Control
Deep reinforcement learning for continuous control has recently achieved impressive progress. However, existing methods often suffer from primacy bias, a tendency to overfit early experiences stored in the replay buffer, which limits an RL agent's sample efficiency and generalizability. In contrast, humans are less susceptible to such bias, partly due to infantile amnesia, where the formation of new neurons disrupts early memory traces, leading to the forgetting of initial experiences. Inspired…

On Pioneering Works of Albert Shiryaev on Markov Decision Processes and Some Later Developments
Eugene A. Feinberg
https://arxiv.org/abs/2506.04896 https:/…

On Pioneering Works of Albert Shiryaev on Markov Decision Processes and Some Later Developments
This article is dedicated to three fundamental papers on Markov Decision Processes and on control with incomplete observations published by Albert Shiryaev approximately sixty years ago. One of these papers was coauthored with O.V. Viskov. We discuss some of the results and some of many rich ideas presented in these papers and survey some later developments. At the end we mention some recent studies of Albert Shiryaev on Kolmogorov's equations for jump Markov processes and on control of continu…

This https://arxiv.org/abs/2411.13983 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

Learning Two-agent Motion Planning Strategies from Generalized Nash Equilibrium for Model Predictive Control
We introduce an Implicit Game-Theoretic MPC (IGT-MPC), a decentralized algorithm for two-agent motion planning that uses a learned value function that predicts the game-theoretic interaction outcomes as the terminal cost-to-go function in a model predictive control (MPC) framework, guiding agents to implicitly account for interactions with other agents and maximize their reward. This approach applies to competitive and cooperative multi-agent motion planning problems which we formulate as const…

Regularization of non-overshooting quasi-continuous sliding mode control for chattering suppression at equilibrium
Michael Ruderman, Denis Efimov
https://arxiv.org/abs/2506.05283 …

Regularization of non-overshooting quasi-continuous sliding mode control for chattering suppression at equilibrium
Robust finite-time feedback controller introduced for the second-order systems in [1] can be seen as a non-overshooting quasi-continuous sliding mode control. The paper proposes a regularization scheme to suppress inherent chattering due to discontinuity of the control [1] in the origin, in favor of practical applications. A detailed analysis with ISS and iISS proofs are provided along with supporting numerical results.

Gate-tunable spectrum and charge dispersion mitigation in a graphene superconducting qubit
Nicolas Aparicio, Simon Messelot, Edgar Bonet-Orozco, Eric Eyraud, Kenji Watanabe, Takashi Taniguchi, Johann Coraux, Julien Renard
https://arxiv.org/abs/2506.04959

Gate-tunable spectrum and charge dispersion mitigation in a graphene superconducting qubit
Controlling the energy spectrum of quantum-coherent superconducting circuits, i.e. the energies of excited states, the circuit anharmonicity and the states' charge dispersion, is essential for designing performant qubits. This control is usually achieved by adjusting the circuit's geometry. In-situ control is traditionally obtained via an external magnetic field, in the case of tunnel Josephson junctions. More recently, semiconductor-weak-links-based Josephson junctions have emerged as an alter…

Measurement of the Granularity of Vowel Production Space By Just Producible Different (JPD) Limens
Peter Viechnicki
https://arxiv.org/abs/2507.02744 https:…

Measurement of the Granularity of Vowel Production Space By Just Producible Different (JPD) Limens
A body of work over the past several decades has demonstrated that the complex and coordinated articulatory movements of human vowel production are governed (at least in part)by control mechanisms whose targets are regions of auditory space. Within the target region control at the sub-phonemic level has also been demonstrated. But the degree of accuracy of that control is unknown. The current work investigates this question by asking how far apart must two vowel stimuli lie in auditory space in…

Autonomous Vehicle Lateral Control Using Deep Reinforcement Learning with MPC-PID Demonstration
Chengdong Wu, Sven Kirchner, Nils Purschke, Alois C. Knoll
https://arxiv.org/abs/2506.04040

Autonomous Vehicle Lateral Control Using Deep Reinforcement Learning with MPC-PID Demonstration
The controller is one of the most important modules in the autonomous driving pipeline, ensuring the vehicle reaches its desired position. In this work, a reinforcement learning based lateral control approach, despite the imperfections in the vehicle models due to measurement errors and simplifications, is presented. Our approach ensures comfortable, efficient, and robust control performance considering the interface between controlling and other modules. The controller consists of the conventi…

Supra-threshold control of peripheral LOD
Benjamin Watson, Neff Walker, Larry F Hodges
https://arxiv.org/abs/2506.22583 https://arxiv…

Supra-threshold control of peripheral LOD
Level of detail (LOD) is widely used to control visual feedback in interactive applications. LOD control is typically based on perception at threshold - the conditions in which a stimulus first becomes perceivable. Yet most LOD manipulations are quite perceivable and occur well above threshold. Moreover, research shows that supra-threshold perception differs drastically from perception at threshold. In that case, should supra-threshold LOD control also differ from LOD control at threshold? In…

Evidence of haze control of Pluto’s atmospheric heat balance from JWST/MIRI thermal light curves: #Pluto

A gate can be really useful for live sound (e.g. when you’re doing a live stream) to cut out unwanted background noise or hum with low latency when you're not speaking. Boz Digital’s “Big Beautiful Door 2” even combines this with a four-band EQ for the closed and open sections and is currently on sale ($49 - 50% with BOZ-JULY-4). I just couldn’t resist… 😅

Control of intervalley scattering in Bi$_2$Te$_3$ via temperature-dependent band renormalization
A. Jabed, F. Goto, B. Frimpong, D. Armanno, A. Longa, M. Michiardi, A. Damascelli, P. Hofmann, G. Jargot, H. Ibrahim, F. L\'egar\'e, N. Gauthier, S. Beaulieu, F. Boschini
https://arxiv.org/abs/2506.03251

Control of intervalley scattering in Bi$_2$Te$_3$ via temperature-dependent band renormalization
The control of out-of-equilibrium electron dynamics in topological insulators is essential to unlock their potential in next-generation quantum technologies. However, the role of temperature on the renormalization of the electronic band structure and, consequently, on electron scattering processes is still elusive. Here, using high-resolution time- and angle-resolved photoemission spectroscopy (TR-ARPES), we show that even a modest ($\sim$15 meV) renormalization of the conduction band of Bi$_2$…

❝Cloudflare, along with a majority of the world's leading publishers and AI companies, is changing the default to block AI crawlers unless they pay creators for their content.❞
Oh!
❝But that's just the beginning. Next, we'll work on a marketplace where content creators and AI companies, large and small, can come together.❞
Oh.
First quote sounds like a strike. Second quote sounds more like a company trying to situate itself as an unavoidable middle player who can control both sides of a market.
https://mastodon.acm.org/@avandeursen/114799313365332691

I know it’s just an overlay vendor looking for a backlink (which I removed) by asking a silly question, but it’s a good opportunity to point out that target size isn’t about (only) touch. It benefits far more users (even voice control):
https://adrianroselli.com/2019/06/targ

Target Size and 2.5.5
TL;DR: Regardless of what accessibility conformance level you target, try to ensure that interactive controls are at least 44 by 44 pixels in size. Links in blocks of text are exempt. Overview In real life there is typically both a visual and tactile component to an interface. You have to…

China's state TV broadcaster has revealed details of a new
"graphite bomb"
that can cause a "complete loss of electricity" across an area of 10,000 square meters,
or knock out entire power stations, the South China Morning Post reported.
An animated video released by China's CCTV showed a missile being launched from a ground-based launcher
and then flying to a target where it releases 90 small submunitions.
They then bounce on t…

Video teases new Chinese blackout bomb that can knock out enemy power stations
Animation shared by state broadcaster shows what appears to be graphite bomb designed to disrupt command and control systems.

Grid-Connected, Data-Driven Inverter Control, Theory to Hardware
Sebastian Graf, Keith Moffat, Anurag Mohapatra, Alessandro Chiuso, Florian D\"orfler
https://arxiv.org/abs/2507.02325

Grid-Connected, Data-Driven Inverter Control, Theory to Hardware
Grid-connected inverter control is challenging to implement due to the difficulty of obtaining and maintaining an accurate grid model. Direct Data-Driven Predictive Control provides a model-free alternative to traditional model-based control methods. This paper describes how the recently-proposed Transient Predictive Control (TPC) can be used for real-world, plug-and-play inverter control. The following hypotheses were tested: 1) The TPC algorithm can be run online using standard hardware, and …

In the US, most working-class people drive. It does not follow that it is pro–working class to double down on car dependency, a debt trap and tool of surveillance and control. https://www.jalopnik.com/1902012/1000-per-month-car-loan-norms/

A $1,000 Per Month Car Loan Is The Norm Now
The share of new-vehicle buyers who are spending over $1,000 is at 19.3%.

This https://arxiv.org/abs/2410.06307 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Model Predictive Control is Almost Optimal for Restless Bandit
We consider the discrete time infinite horizon average reward restless markovian bandit (RMAB) problem. We propose a \emph{model predictive control} based non-stationary policy with a rolling computational horizon $τ$. At each time-slot, this policy solves a $τ$ horizon linear program whose first control value is kept as a control for the RMAB. Our solution requires minimal assumptions and quantifies the loss in optimality in terms of $τ$ and the number of arms, $N$. We show that its sub-opt…

Series B, Episode 13 - Star One
DURKIM: Not computers. Computer. Singular. Very singular indeed. Our unbeatable control and coordination centre.
SERVALAN: No!
DURKIM: Servalan, by design or accident Star One is failing.
https://blake.torpidity.net/m/213/29 B7B5

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

This https://arxiv.org/abs/2506.01835 has been replaced.
initial toot: https://mastoxiv.page/@ar…

Discrete vortex-based broadcast mode analysis for modification of dynamic stall
We integrate a discrete vortex method with complex network analysis to strategize dynamic stall mitigation over a pitching airfoil with active flow control. The objective is to inform actuator placement and timing to introduce control inputs during the transient evolution of dynamic stall. To this end, we represent the massively separated flow as a network of discrete vortical elements and quantify the interactions among these vortical nodes by tracking the spread of displacement perturbations …

This https://arxiv.org/abs/2406.02436 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Safe, Out-of-Distribution-Adaptive MPC with Conformalized Neural Network Ensembles
We present SODA-MPC, a Safe, Out-of-Distribution-Adaptive Model Predictive Control algorithm, which uses an ensemble of learned models for prediction, with a runtime monitor to flag unreliable out-of-distribution (OOD) predictions. When an OOD situation is detected, SODA-MPC triggers a safe fallback control strategy based on reachability, yielding a control framework that achieves the high performance of learning-based models while preserving the safety of reachability-based control. We demonst…

Virtual Polarization Elements for Spatially Varying Jones Matrix Transformations on a Free-Space Plane
Mingyue Wang (Department of Physics, Xiamen University, Xiamen, China), Jiayuan Wang (Department of Physics, Xiamen University, Xiamen, China, Shenzhen Research Institute of Xiamen University, Shenzhen, China)
https://arxiv.org…

Virtual Polarization Elements for Spatially Varying Jones Matrix Transformations on a Free-Space Plane
Precise control over the spatial and polarization properties of light is foundational for advanced photonic systems, yet most conventional approaches are constrained to local, contact-based manipulation at physical interfaces. To overcome these constraints, here we introduce a fundamentally new framework for action-at-a-distance polarization control using virtual polarization elements (VPEs). VPEs apply prescribed local Jones matrix transformations between an input field at the modulation plane…

AnyI2V: Animating Any Conditional Image with Motion Control
Ziye Li, Hao Luo, Xincheng Shuai, Henghui Ding
https://arxiv.org/abs/2507.02857 https://…

AnyI2V: Animating Any Conditional Image with Motion Control
Recent advancements in video generation, particularly in diffusion models, have driven notable progress in text-to-video (T2V) and image-to-video (I2V) synthesis. However, challenges remain in effectively integrating dynamic motion signals and flexible spatial constraints. Existing T2V methods typically rely on text prompts, which inherently lack precise control over the spatial layout of generated content. In contrast, I2V methods are limited by their dependence on real images, which restricts…

Video Quality Monitoring for Remote Autonomous Vehicle Control
Dimitrios Kafetzis, Nikos Fotiou, Savvas Argyropoulos, Jad Nasreddine, Iordanis Koutsopoulos
https://arxiv.org/abs/2506.03166

Video Quality Monitoring for Remote Autonomous Vehicle Control
The delivery of high-quality, low-latency video streams is critical for remote autonomous vehicle control, where operators must intervene in real time. However, reliable video delivery over Fourth/Fifth-Generation (4G/5G) mobile networks is challenging due to signal variability, mobility-induced handovers, and transient congestion. In this paper, we present a comprehensive blueprint for an integrated video quality monitoring system, tailored to remote autonomous vehicle operation. Our proposed …

TikTok expands Manage Topics, a feature that lets users customize how often specific topics appear in the For You feed, and adds AI-powered keyword filtering (Emma Roth/The Verge)
https://www.theverge.com/news/678779/tiktok-for-you-feed-manage-…

TikTok gives everyone more control over what’s on their For You page
TikTok is rolling out Manage Topics globally, a feature that lets you control how often you see certain kinds of videos in your For You feed, such as those related to sports, travel, humor, or dance.

This https://arxiv.org/abs/2407.13257 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Predictive control for nonlinear stochastic systems: Closed-loop guarantees with unbounded noise
We present a stochastic model predictive control framework for nonlinear systems subject to unbounded process noise with closed-loop guarantees. First, we provide a conceptual shrinking-horizon framework that utilizes general probabilistic reachable sets and minimizes the expected cost. Then, we provide a tractable receding-horizon formulation that uses a nominal state to minimize a deterministic quadratic cost and satisfy tightened constraints. Our theoretical analysis demonstrates recursive f…

A Fundamental Bound for Robust Quantum Gate Control
Robert L. Kosut, Daniel A. Lidar, Herschel Rabitz
https://arxiv.org/abs/2507.01215 https://

A Fundamental Bound for Robust Quantum Gate Control
We derive a universal performance limit for coherent quantum control in the presence of modeled and unmodeled uncertainties. For any target unitary $W$ that is implementable in the absence of error, we prove that the worst-case (and hence the average) gate fidelity obeys the lower bound $F \ge \Flb\bigl(\tf \Omeff\bigr)$, where $\tf$ is the gate duration and $\Omeff$ is a single frequency-like measure that aggregates \emph{all} bounded uncertainty sources, e.g., coherent control imperfections, …

Energy Consumption Optimization for Autonomous Vehicles via Positive Control Input Minimization
Andreas Hadjigeorgiou, Stelios Timotheou
https://arxiv.org/abs/2506.04685

Energy Consumption Optimization for Autonomous Vehicles via Positive Control Input Minimization
Autonomous vehicles (AVs) present a unique opportunity to improve the sustainability of transportation systems by adopting eco-driving strategies that reduce energy consumption and emissions. This paper introduces a novel surrogate model for energy and fuel consumption that minimizes Positive Control Input (PCI). Unlike conventional objectives such as squared acceleration, which often misrepresent actual energy usage, PCI provides a more accurate and optimization-friendly alternative. Building …

Neuronal avalanches as a predictive biomarker of BCI performance: towards a tool to guide tailored training program
Camilla Mannino (NERV), Pierpaolo Sorrentino (INS), Mario Chavez (NERV), Marie-Costance Corsi (NERV)
https://arxiv.org/abs/2506.04745

Neuronal avalanches as a predictive biomarker of BCI performance: towards a tool to guide tailored training program
Brain-Computer Interfaces (BCIs) based on motor imagery (MI) hold promise for restoring control in individuals with motor impairments. However, up to 30% of users remain unable to effectively use BCIs-a phenomenon termed ''BCI inefficiency.'' This study addresses a major limitation in current BCI training protocols: the use of fixed-length training paradigms that ignore individual learning variability. We propose a novel approach that leverages neuronal avalanches-spatiotemporal cascades of bra…

A New Perspective On AI Safety Through Control Theory Methodologies
Lars Ullrich, Walter Zimmer, Ross Greer, Knut Graichen, Alois C. Knoll, Mohan Trivedi
https://arxiv.org/abs/2506.23703

A New Perspective On AI Safety Through Control Theory Methodologies
While artificial intelligence (AI) is advancing rapidly and mastering increasingly complex problems with astonishing performance, the safety assurance of such systems is a major concern. Particularly in the context of safety-critical, real-world cyber-physical systems, AI promises to achieve a new level of autonomy but is hampered by a lack of safety assurance. While data-driven control takes up recent developments in AI to improve control systems, control theory in general could be leveraged t…

Through the Stealth Lens: Rethinking Attacks and Defenses in RAG
Sarthak Choudhary, Nils Palumbo, Ashish Hooda, Krishnamurthy Dj Dvijotham, Somesh Jha
https://arxiv.org/abs/2506.04390

Through the Stealth Lens: Rethinking Attacks and Defenses in RAG
Retrieval-augmented generation (RAG) systems are vulnerable to attacks that inject poisoned passages into the retrieved set, even at low corruption rates. We show that existing attacks are not designed to be stealthy, allowing reliable detection and mitigation. We formalize stealth using a distinguishability-based security game. If a few poisoned passages are designed to control the response, they must differentiate themselves from benign ones, inherently compromising stealth. This motivates th…

Series B, Episode 07 - Killer
JENNA: A missile?
BLAKE: No, it can't be. They can't have spotted Liberator. We've got the blind down. [launch appears on screen]
JENNA: There it is, look, just leaving the ionosphere.
https://blake.torpidity.net/m/207/33 B7B6…

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

This https://arxiv.org/abs/2411.14321 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Continual Learning and Lifting of Koopman Dynamics for Linear Control of Legged Robots
The control of legged robots, particularly humanoid and quadruped robots, presents significant challenges due to their high-dimensional and nonlinear dynamics. While linear systems can be effectively controlled using methods like Model Predictive Control (MPC), the control of nonlinear systems remains complex. One promising solution is the Koopman Operator, which approximates nonlinear dynamics with a linear model, enabling the use of proven linear control techniques. However, achieving accurat…

RichControl: Structure- and Appearance-Rich Training-Free Spatial Control for Text-to-Image Generation
Liheng Zhang, Lexi Pang, Hang Ye, Xiaoxuan Ma, Yizhou Wang
https://arxiv.org/abs/2507.02792

RichControl: Structure- and Appearance-Rich Training-Free Spatial Control for Text-to-Image Generation
Text-to-image (T2I) diffusion models have shown remarkable success in generating high-quality images from text prompts. Recent efforts extend these models to incorporate conditional images (e.g., depth or pose maps) for fine-grained spatial control. Among them, feature injection methods have emerged as a training-free alternative to traditional fine-tuning approaches. However, they often suffer from structural misalignment, condition leakage, and visual artifacts, especially when the condition …

Cloud-Based Interoperability in Residential Energy Systems
Darren Leniston, David Ryan, Ammar Malik, Jack Jackman, Terence O'Donnell
https://arxiv.org/abs/2506.05076

Cloud-Based Interoperability in Residential Energy Systems
As distributed energy resources (DERs) such as solar PV, batteries and electric vehicles become increasingly prevalent at the edge, maintaining grid stability requires advanced monitoring and control mechanisms. This paper presents a scalable smart grid gateway architecture that enables interoperability between Modbus-based inverters and IEEE 2030.5 cloud-based control systems. The proposed solution leverages Azure cloud services and edge-computing gateway devices to support dynamic configurati…

QUIC Delay Control: an implementation of congestion and delay control
Saverio Mascolo, Andrea Vittorio Balillo, Gioacchino Manfredi, Davide D'Agostino, Luca De Cicco
https://arxiv.org/abs/2507.00896

QUIC Delay Control: an implementation of congestion and delay control
A new congestion and delay control algorithm named QUIC Delay Control (QUIC-DC) is proposed for controlling not only congestion but also the queuing delay encountered along the forward communication path. The core idea is to estimate the one-way queuing delay of a connection to trigger an early reaction to congestion. This idea, along with the TCP Westwood+ congestion control algorithm, has been implemented in QUIC-DC and compared with QUIC Cubic, BBRv2, NewReno, Westwood+. The results obtained…

Phase-based Nonlinear Model Predictive Control for Humanoid Walking Stabilization with Single and Double Support Time Adjustments
Kwanwoo Lee, Gyeongjae Park, Jaeheung Park
https://arxiv.org/abs/2506.03856

Phase-based Nonlinear Model Predictive Control for Humanoid Walking Stabilization with Single and Double Support Time Adjustments
Balance control for humanoid robots has been extensively studied to enable robots to navigate in real-world environments. However, balance controllers that explicitly optimize the durations of both the single support phase, also known as step timing, and the Double Support Phase (DSP) have not been widely explored due to the inherent nonlinearity of the associated optimization problem. Consequently, many recent approaches either ignore the DSP or adjust its duration based on heuristics or on li…

This https://arxiv.org/abs/2405.10886 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Three-mode tunable coupler for superconducting two-qubit gates
Building a scalable universal high-performance quantum processor is a formidable challenge. In particular, the problem of realizing fast high-perfomance two-qubit gates of high-fidelity remains needful. Here we propose a building block for a scalable quantum processor consisting of two transmons and a tunable three-mode coupler allowing for a ZZ interaction control. We experimentally demonstrate the native CZ gate with the pulse duration of 60 ns achieving the two-qubit gate fidelity above 98%,…

A Data-Driven Prescribed-Time Control Framework via Koopman Operator and Adaptive Backstepping
Yue Wu
https://arxiv.org/abs/2507.02549 https://

A Data-Driven Prescribed-Time Control Framework via Koopman Operator and Adaptive Backstepping
Achieving rapid and time-deterministic stabilization for complex systems characterized by strong nonlinearities and parametric uncertainties presents a significant challenge. Traditional model-based control relies on precise system models, whereas purely data-driven methods often lack formal stability guarantees, limiting their applicability in safety-critical systems. This paper proposes a novel control framework that synergistically integrates data-driven modeling with model-based control. Th…

This https://arxiv.org/abs/2505.24198 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning Gentle Humanoid Locomotion and End-Effector Stabilization Control
Can your humanoid walk up and hand you a full cup of beer, without spilling a drop? While humanoids are increasingly featured in flashy demos like dancing, delivering packages, traversing rough terrain, fine-grained control during locomotion remains a significant challenge. In particular, stabilizing a filled end-effector (EE) while walking is far from solved, due to a fundamental mismatch in task dynamics: locomotion demands slow-timescale, robust control, whereas EE stabilization requires rap…

A learning model predictive control for virtual coupling in railroads
Miguel A. Vaquero-Serrano, Francesco Borrelli, Jesus Felez
https://arxiv.org/abs/2507.02383

A learning model predictive control for virtual coupling in railroads
The objective of this paper is to present a novel intelligent train control system for virtual coupling in railroads based on a Learning Model Predictive Control (LMPC). Virtual coupling is an emerging railroad technology that reduces the distance between trains to increase the capacity of the line, whereas LMPC is an optimization-based controller that incorporates artificial intelligence methods to improve its control policies. By incorporating data from past experiences into the optimization …

Series B, Episode 05 - Pressure Point
JENNA: It's over an hour since our last contact.
CALLY: It can't be a communicator fault.
JENNA: Not on all four bracelets, no.
CALLY: What do you think has happened?
JENNA: They've run into trouble.
https://blake.torpidity.net/m/205/407

Blakes7Bot | Search
Companion site to the Blakes7Bot Twitter, Mastodon, and Bluesky accounts. Automated lines of Blakes 7 dialog. Includes a script search facility.

Optimal control of the Poisson equation with transport regularization: Properties of optimal transport plans and transport map
Christian Meyer, Gerd Wachsmuth
https://arxiv.org/abs/2506.02808

Optimal control of the Poisson equation with transport regularization: Properties of optimal transport plans and transport map
An optimal control problem in the space of Borel measures governed by the Poisson equation is investigated. The characteristic feature of the problem under consideration is the Tikhonov regularization term in form of the transportation distance of the control to a given prior. Existence of optimal solutions is shown and first-order necessary optimality conditions are derived. The latter are used to deduce structural a priori information about the optimal control and its support based on propert…

Integrating path-planning and control for robotic unicycles
M\'at\'e B. Vizi, D\'enes T\'ak\'acs, G\'abor St\'ep\'an, G\'abor Orosz
https://arxiv.org/abs/2507.02700

Integrating path-planning and control for robotic unicycles
This article focuses on integrating path-planning and control with specializing on the unique needs of robotic unicycles. A unicycle design is presented which is capable of accelerating/breaking and carrying out a variety of maneuvers. The proposed path-planning method segments the path into straight and curved path sections dedicated for accelerating/breaking and turning maneuvers, respectively. The curvature profiles of the curved sections are optimized while considering the control performan…

Nonlinear Optimal Control of DC Microgrids with Safety and Stability Guarantees
Muratkhan Abdirash, Xiaofan Cui
https://arxiv.org/abs/2506.03454 https://…

Nonlinear Optimal Control of DC Microgrids with Safety and Stability Guarantees
A DC microgrid is a promising alternative to the traditional AC power grid, since it can efficiently integrate distributed and renewable energy resources. However, as an emerging framework, it lacks the rigorous theoretical guarantees of its AC counterpart. In particular, safe stabilization of the DC microgrid has been a non-trivial task in power electronics. To address that, we take a control theoretic perspective in designing the feedback controller with provable guarantees. We present a syst…

Multilevel Stochastic Gradient Descent for Optimal Control Under Uncertainty
Niklas Baumgarten, David Schneiderhan
https://arxiv.org/abs/2506.02647 https:/…

Multilevel Stochastic Gradient Descent for Optimal Control Under Uncertainty
We present a multilevel stochastic gradient descent method for the optimal control of systems governed by partial differential equations under uncertain input data. The gradient descent method used to find the optimal control leverages a parallel multilevel Monte Carlo method as stochastic gradient estimator. As a result, we achieve precise control over the stochastic gradient's bias, introduced by numerical approximation, and its sampling error, arising from the use of incomplete gradients, wh…

This https://arxiv.org/abs/2503.14727 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Beacon-Based Feedback Control for Parking an Active-Joint Center-Articulated Mobile Robot
This paper presents an autonomous parking control strategy for an active-joint center-articulated mobile robot. We first derive a kinematic model of the robot, then propose a control law to stabilize the vehicle's configuration within a small neighborhood of the goal. The control law, designed using Lyapunov techniques, is based on the robot's polar coordinate equations. A beacon-based guidance system provides feedback on the target's position and orientation. Simulations demonstrate the robot'…

Geometrization of Higher-Order Linear Control Laws for Attitude Control on $\mathsf{SO(3)}$
Farooq Aslam, Hafiz Zeeshan Iqbal Khan, Muhammad Farooq Haydar, Suhail Akhtar, Jamshed Riaz
https://arxiv.org/abs/2507.00997

Geometrization of Higher-Order Linear Control Laws for Attitude Control on $\mathsf{SO(3)}$
This paper presents a theoretical framework for analyzing the stability of higher-order geometric nonlinear control laws for attitude control on the Special Orthogonal Group $\mathrm{SO(3)}$. In particular, the paper extends existing results on the analysis of PID-type geometric nonlinear control laws to more general higher-order dynamic state-feedback compensators on $\mathrm{SO(3)}$. The candidate Lyapunov function is motivated by quadratic Lyapunov functions of the form $V(x)=x^{\top}Px$ typ…

Geometric Visual Servo Via Optimal Transport
Ethan Canzini, Simon Pope, Ashutosh Tiwari
https://arxiv.org/abs/2506.02768 https://arxi…

Geometric Visual Servo Via Optimal Transport
When developing control laws for robotic systems, the principle factor when examining their performance is choosing inputs that allow smooth tracking to a reference input. In the context of robotic manipulation, this involves translating an object or end-effector from an initial pose to a target pose. Robotic manipulation control laws frequently use vision systems as an error generator to track features and produce control inputs. However, current control algorithms don't take into account the …

Optimality Loss Minimization in Distributed Control with Application to District Heating
Audrey Blizard, Stephanie Stockar
https://arxiv.org/abs/2507.02144

Optimality Loss Minimization in Distributed Control with Application to District Heating
This paper presents a novel partitioning method designed to minimize control performance degradation resulting from partitioning a system for distributed control while maintaining the computational benefits of these methods. A game-theoretic performance metric, the modified Price of Anarchy, is introduced and is used in a generalizable partitioning metric to quantify optimality losses in a distributed controller. By finding the partition that minimizes the partitioning metric, the best-performi…

The Bias of Subspace-based Data-Driven Predictive Control
Keith Moffat, Florian D\"orfler, Alessandro Chiuso
https://arxiv.org/abs/2507.02468 https://…

The Bias of Subspace-based Data-Driven Predictive Control
This paper quantifies and addresses the bias of subspace-based Data-Driven Predictive Control (DDPC) for linear, time-invariant (LTI) systems. The primary focus is the bias that arises when the training data is gathered with a feedback controller in closed-loop with the system. First, the closed-loop bias of Subspace Predictive Control is quantified using the training data innovations. Next, the bias of direct, subspace-based DDPC methods DeePC and $γ$-DDPC is shown to consist of two parts--th…

This https://arxiv.org/abs/2308.08478 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Episodic Bayesian Optimal Control with Unknown Randomness Distributions
Stochastic optimal control with unknown randomness distributions has been studied for a long time, encompassing robust control, distributionally robust control, and adaptive control. We propose a new episodic Bayesian approach that incorporates Bayesian learning with optimal control. In each episode, the approach learns the randomness distribution with a Bayesian posterior and subsequently solves the corresponding Bayesian average estimate of the true problem. The resulting policy is exercised …

This https://arxiv.org/abs/2408.11978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Optimized Kalman Filter based State Estimation and Height Control in Hopping Robots
Rotor-based hopping locomotion significantly improves efficiency and operation time as compared to purely flying systems; where most hopping robots use the liftoff states and an assumed ballistic trajectory to determine the hopping height. However, significant aerial phase force (e.g., thrust and drag) can invalidate this assumption and lead to poor estimation performance. To combat this issue, a group has implemented multiple sensors (active and passive optical, inertial, and contact) and sign…

Robust feedback-based quantum optimization: analysis of coherent control errors
Mirko Legnini, Julian Berberich
https://arxiv.org/abs/2507.02532 https://…

Robust feedback-based quantum optimization: analysis of coherent control errors
The Feedback-based Algorithm for Quantum Optimization (FALQON) is a Lyapunov inspired quantum algorithm proposed to tackle combinatorial optimization problems. In this paper, we examine the robustness of FALQON against coherent control errors, a class of multiplicative errors that affect the control input. We show that the algorithm is asymptotically robust with respect to systematic errors, and we derive robustness bounds for independent errors. Finally, we propose a robust version of FALQON w…

HAC-LOCO: Learning Hierarchical Active Compliance Control for Quadruped Locomotion under Continuous External Disturbances
Xiang Zhou, Xinyu Zhang, Qingrui Zhang
https://arxiv.org/abs/2507.02447

HAC-LOCO: Learning Hierarchical Active Compliance Control for Quadruped Locomotion under Continuous External Disturbances
Despite recent remarkable achievements in quadruped control, it remains challenging to ensure robust and compliant locomotion in the presence of unforeseen external disturbances. Existing methods prioritize locomotion robustness over compliance, often leading to stiff, high-frequency motions, and energy inefficiency. This paper, therefore, presents a two-stage hierarchical learning framework that can learn to take active reactions to external force disturbances based on force estimation. In the…

[2025-07-07 Mon (UTC), no new articles found for math.OC Optimization and Control]
toXiv_bot_toot

This https://arxiv.org/abs/2505.10033 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Evaluating Robustness of Deep Reinforcement Learning for Autonomous Surface Vehicle Control in Field Tests
Despite significant advancements in Deep Reinforcement Learning (DRL) for Autonomous Surface Vehicles (ASVs), their robustness in real-world conditions, particularly under external disturbances, remains insufficiently explored. In this paper, we evaluate the resilience of a DRL-based agent designed to capture floating waste under various perturbations. We train the agent using domain randomization and evaluate its performance in real-world field tests, assessing its ability to handle unexpected…

Lie algebra rank condition for bilinear control systems on $\mathbb{R}^2$
Efrain Cruz-Mullisaca, Victor H. Patty-Yujra
https://arxiv.org/abs/2506.02428 htt…

Lie algebra rank condition for bilinear control systems on $\mathbb{R}^2$
We will study the controllability problem of a bilinear control system on $\mathbb{R}^2:$ the main result is the characterization of the Lie algebra rank condition for the system. On the other hand, using elementary techniques, we recover conditions for the controllability of the induced angular system on the projective space. Finally, we will give controllability criteria for the system.

This https://arxiv.org/abs/2505.20573 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Collision- and Reachability-Aware Multi-Robot Control with Grounded LLM Planners
Large language models (LLMs) have demonstrated strong performance in various robot control tasks. However, their deployment in real-world applications remains constrained. Even state-ofthe-art LLMs, such as GPT-o4mini, frequently produce invalid action plans that violate physical constraints, such as directing a robot to an unreachable location or causing collisions between robots. This issue primarily arises from a lack of awareness of these physical constraints during the reasoning process. T…

Learning-based primal-dual optimal control of discrete-time stochastic systems with multiplicative noise
Xiushan Jiang, Weihai Zhang
https://arxiv.org/abs/2506.02613

Learning-based primal-dual optimal control of discrete-time stochastic systems with multiplicative noise
Reinforcement learning (RL) is an effective approach for solving optimal control problems without knowing the exact information of the system model. However, the classical Q-learning method, a model-free RL algorithm, has its limitations, such as lack of strict theoretical analysis and the need for artificial disturbances during implementation. This paper explores the partially model-free stochastic linear quadratic regulator (SLQR) problem for a system with multiplicative noise from the primal…

This https://arxiv.org/abs/2506.01759 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

ADEPT: Adaptive Diffusion Environment for Policy Transfer Sim-to-Real
Model-free reinforcement learning has emerged as a powerful method for developing robust robot control policies capable of navigating through complex and unstructured environments. The effectiveness of these methods hinges on two essential elements: (1) the use of massively parallel physics simulations to expedite policy training, and (2) an environment generator tasked with crafting sufficiently challenging yet attainable environments to facilitate continuous policy improvement. Existing metho…

Mirror descent for constrained stochastic control problems
Deven Sethi, David \v{S}i\v{s}ka
https://arxiv.org/abs/2506.02564 https://…

Mirror descent for constrained stochastic control problems
Mirror descent is a well established tool for solving convex optimization problems with convex constraints. This article introduces continuous-time mirror descent dynamics for approximating optimal Markov controls for stochastic control problems with the action space being bounded and convex. We show that if the Hamiltonian is uniformly convex in its action variable then mirror descent converges linearly while if it is uniformly strongly convex relative to an appropriate Bregman divergence, the…

SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL
Jiaheng Hu, Peter Stone, Roberto Mart\'in-Mart\'in
https://arxiv.org/abs/2506.04147

SLAC: Simulation-Pretrained Latent Action Space for Whole-Body Real-World RL
Building capable household and industrial robots requires mastering the control of versatile, high-degree-of-freedom (DoF) systems such as mobile manipulators. While reinforcement learning (RL) holds promise for autonomously acquiring robot control policies, scaling it to high-DoF embodiments remains challenging. Direct RL in the real world demands both safe exploration and high sample efficiency, which are difficult to achieve in practice. Sim-to-real RL, on the other hand, is often brittle du…

Approximate Solution Methods for the Average Reward Criterion in Optimal Tracking Control of Linear Systems
Duc Cuong Nguyen
https://arxiv.org/abs/2507.01556

Approximate Solution Methods for the Average Reward Criterion in Optimal Tracking Control of Linear Systems
This paper studies optimal control under the average-reward/cost criterion for deterministic linear systems. We derive the value function and optimal policy, and propose an approximate solution using Model Predictive Control to enable practical implementation.

This https://arxiv.org/abs/2409.17469 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

VertiSelector: Automatic Curriculum Learning for Wheeled Mobility on Vertically Challenging Terrain
Reinforcement Learning (RL) has the potential to enable extreme off-road mobility by circumventing complex kinodynamic modeling, planning, and control by simulated end-to-end trial-and-error learning experiences. However, most RL methods are sample-inefficient when training in a large amount of manually designed simulation environments and struggle at generalizing to the real world. To address these issues, we introduce VertiSelector (VS), an automatic curriculum learning framework designed to …

This https://arxiv.org/abs/2309.14792 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Exploiting Local Observations for Robust Robot Learning
While many robotic tasks can be addressed through either centralized single-agent control with full state observation or decentralized multi-agent control, clear criteria for selecting the optimal approach are lacking. This paper presents a comprehensive investigation into how multi-agent reinforcement learning (MARL) with local observations can enhance robustness in complex robotic systems compared to traditional centralized control methods. We provide both theoretical analysis and empirical v…

Reinforcement Learning for Discrete-time LQG Mean Field Social Control Problems with Unknown Dynamics
Hanfang Zhang, Bing-Chang Wang, Shuo Chen
https://arxiv.org/abs/2507.01420

Reinforcement Learning for Discrete-time LQG Mean Field Social Control Problems with Unknown Dynamics
This paper studies the discrete-time linear-quadratic-Gaussian mean field (MF) social control problem in an infinite horizon, where the dynamics of all agents are unknown. The objective is to design a reinforcement learning (RL) algorithm to approximate the decentralized asymptotic optimal social control in terms of two algebraic Riccati equations (AREs). In this problem, a coupling term is introduced into the system dynamics to capture the interactions among agents. This causes the equivalence…

This https://arxiv.org/abs/2505.00237 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Future-Oriented Navigation: Dynamic Obstacle Avoidance with One-Shot Energy-Based Multimodal Motion Prediction
This paper proposes an integrated approach for the safe and efficient control of mobile robots in dynamic and uncertain environments. The approach consists of two key steps: one-shot multimodal motion prediction to anticipate motions of dynamic obstacles and model predictive control to incorporate these predictions into the motion planning process. Motion prediction is driven by an energy-based neural network that generates high-resolution, multi-step predictions in a single operation. The pred…

This https://arxiv.org/abs/2409.11962 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Reactive Collision Avoidance for Safe Agile Navigation
Reactive collision avoidance is essential for agile robots navigating complex and dynamic environments, enabling real-time obstacle response. However, this task is inherently challenging because it requires a tight integration of perception, planning, and control, which traditional methods often handle separately, resulting in compounded errors and delays. This paper introduces a novel approach that unifies these tasks into a single reactive framework using solely onboard sensing and computing.…