Tootfinder

ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
Junkai Jiang, Yihe Chen, Yibin Yang, Ruochen Li, Shaobing Xu, Jianqiang Wang
https://arxiv.org/abs/2508.09581

ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
Multi-vehicle trajectory planning (MVTP) is one of the key challenges in multi-robot systems (MRSs) and has broad applications across various fields. This paper presents ESCoT, an enhanced step-based coordinate trajectory planning method for multiple car-like robots. ESCoT incorporates two key strategies: collaborative planning for local robot groups and replanning for duplicate configurations. These strategies effectively enhance the performance of step-based MVTP methods. Through extensive ex…

LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planning
Shibo Sun, Xue Li, Donglin Di, Mingjie Wei, Lanshun Nie, Wei-Nan Zhang, Dechen Zhan, Yang Song, Lei Fan
https://arxiv.org/abs/2507.08496

LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planning
While large language models (LLMs) have advanced procedural planning for embodied AI systems through strong reasoning abilities, the integration of multimodal inputs and counterfactual reasoning remains underexplored. To tackle these challenges, we introduce LLaPa, a vision-language model framework designed for multimodal procedural planning. LLaPa generates executable action sequences from textual task descriptions and visual environmental images using vision-language models (VLMs). Furthermor…

I think the cats are planning something... :ablobcathungry:⁠:ablobcathungry:

Klarna, which is planning a potential NY IPO as soon as September, is focusing on its banking push to boost revenue while shedding its image as a BNPL phenom (Aisha S Gani/Bloomberg)
https://www.bloomberg.com/news/articles/2025-08-11/klarna-r…

Klarna Revives Its IPO, But This Time as a Bank
As the payments giant prepares an IPO, it’s trying to persuade millions of consumers to use its debit cards.

Contested Route Planning
Jakub \v{C}ern\'y, Garud Iyengar, Christian Kroer
https://arxiv.org/abs/2508.10189 https://arxiv.org/pdf/2508.10189

Contested Route Planning
We consider the problem of routing for logistics purposes, in a contested environment where an adversary attempts to disrupt the vehicle along the chosen route. We construct a game-theoretic model that captures the problem of optimal routing in such an environment. While basic robust deterministic routing plans are already challenging to devise, they tend to be predictable, which can limit their effectiveness. By introducing calculated randomness via modeling the route planning process as a two…

Reasoning About Knowledge on Regular Expressions is 2EXPTIME-complete
Avijeet Ghosh, Sujata Ghosh, Fran\c{c}ois Schwarzentruber
https://arxiv.org/abs/2508.09784 https://

Reasoning About Knowledge on Regular Expressions is 2EXPTIME-complete
Logics for reasoning about knowledge and actions have seen many applications in various domains of multi-agent systems, including epistemic planning. Change of knowledge based on observations about the surroundings forms a key aspect in such planning scenarios. Public Observation Logic (POL) is a variant of public announcement logic for reasoning about knowledge that gets updated based on public observations. Each state in an epistemic (Kripke) model is equipped with a set of expected observati…

Planning to bike to work tomorrow but the part of the Oak Leaf Trail I normally ride on to avoid street traffic is probably underwater along the Menomonee River...

https://waterdata.usgs.gov/monitoring-location/USGS-04087120/#d…

Sooo... I was in town and this guy looked at me funny. I'm pretty sure he was planning something.
So I shot him dead.
What do you mean I was the aggressor? What do you mean 'murder is illegal, you striking first is not self defence?'
He was blatantly shifty.
^ THE ENTIRE WESTERN MEDIA TODAY

Would love to hear migration thoughts & plans (if any) from other FOSS maintainers (and sponsors!) currently using Github Sponsors. Are you planning/supporting to leave? Are you afraid you'll lose sponsors?
Personally, I'd also love to hear from my own sponsors (some of which are also here 👋). I'm considering migrating various public & still private htt…

Open source building blocks for computational design. Est. 2006
thi.ng is a set of ~350 complementing code libraries & projects, a long-term effort devoted to Computational Design in its many forms.

White House officials said they would review the Smithsonian’s exhibition text, curation, exhibition planning and collections,
starting with eight museums
“The Smithsonian’s work is grounded in a deep commitment to scholarly excellence, rigorous research, and the accurate, factual presentation of history,”
a Smithsonian spokesperson said in a statement Tuesday afternoon.
“We are reviewing the letter with this commitment in mind and will continue to collaborate constr…

White House announces more aggressive review of Smithsonian museums
In a letter to the Smithsonian’s leader, White House officials said they would review exhibition text, curation, exhibition planning and collections starting with eight museums.

Reminder: public protest is about visibility, not hiding. The power comes from people being willing to be visible, put their time on the line, and if needed, their bodies.
If you're going to start shit, maybe just ... don't. Direct action of the illegal sort requires careful planning and a tactical and strategic plan for why it will win. It happens sometimes, but it's not the mode, it's not the mode of the No Kings protests. This is a popular, public demonstration of outrage. It's legal (for now, and I hope, ever).
Yes, the police may try to kettle protesters and then trap them into curfew violations in places that have curfews. Masks won't save us from that where it happens. Just running the system with competent representation (and enough bodies to make it A Problem for the system) will.
We don't all need to be masked vigilantes at protests, and in a lot of cases, we need to be worried about the people masking up and starting shit. Remember that this is our opponents current tactic: masked, violent attacks. Don't look like them.

FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation
Yuxuan Jiang, Zehua Chen, Zeqian Ju, Chang Li, Weibei Dou, Jun Zhu
https://arxiv.org/abs/2507.08557

FreeAudio: Training-Free Timing Planning for Controllable Long-Form Text-to-Audio Generation
Text-to-audio (T2A) generation has achieved promising results with the recent advances in generative models. However, because of the limited quality and quantity of temporally-aligned audio-text pairs, existing T2A methods struggle to handle the complex text prompts that contain precise timing control, e.g., "owl hooted at 2.4s-5.2s". Recent works have explored data augmentation techniques or introduced timing conditions as model inputs to enable timing-conditioned 10-second T2A generation, whi…

"If you’re new to organizing protests, check out our Protest Planning Toolkit at
https://www.fiftyfifty.one/organizer-resources"
Original post:

O.k., more screencaps from Michael Zeiler's #SolarEclipse Conference talk: stills from animations of the umbra moving over Spain in 2026 and 2027, setting the stage for tomorrow when most of the sessions will deal with future eclipses and planning. This summer a new website, EclipseAtlas.com (not up yet) will collect all old and new eclipse maps material. 9/n

Route Planning and Online Routing for Quantum Key Distribution Networks
Jorge L\'opez, Charalampos Chatzinakis, Marc Cartigny
https://arxiv.org/abs/2508.09735 https://

Route Planning and Online Routing for Quantum Key Distribution Networks
Quantum Key Distribution (QKD) networks harness the principles of quantum physics in order to securely transmit cryptographic key material, providing physical guarantees. These networks require traditional management and operational components, such as routing information through the network elements. However, due to the limitations on capacity and the particularities of information handling in these networks, traditional shortest paths algorithms for routing perform poorly on both route planni…

Decision-Making-Based Path Planning for Autonomous UAVs: A Survey
Kelen C. Teixeira Vivaldini, Robert P\v{e}ni\v{c}ka, Martin Saska
https://arxiv.org/abs/2508.09304 https://

Decision-Making-Based Path Planning for Autonomous UAVs: A Survey
One of the most critical features for the successful operation of autonomous UAVs is the ability to make decisions based on the information acquired from their surroundings. Each UAV must be able to make decisions during the flight in order to deal with uncertainties in its system and the environment, and to further act upon the information being received. Such decisions influence the future behavior of the UAV, which is expressed as the path plan. Thus, decision-making in path planning is an e…

You know you've been together too long when you book your covid shots in on your anniversary. 🤦‍♀️
But I did check and we are planning on going to lunch afterwards.
We've been together since we were 22. I have absolutely no idea how that happened - it wasn't planned. But here we are - 41 years later. Bloody hell!
#Anniversary

Patient-Specific 3D Printed Dynamic Preoperative Planning Models in Modern Medicine
Keshav Jha, Joseph Mayer
https://arxiv.org/abs/2508.10134 https://arxiv…

Patient-Specific 3D Printed Dynamic Preoperative Planning Models in Modern Medicine
Three-dimensional (3D) printed preoperative planning models serve a critical role in the success of many medical procedures. However, many of these models do not portray the patient's complete anatomy due to their monolithic and static nature. The use of dynamic 3D-printed models can better equip physicians by providing a more anatomically accurate model due to its movement capabilities and the ability to remove and replace printed anatomies based on planning stages. A dynamic 3D-printed preope…

A Unified Framework from Boltzmann Transport to Proton Treatment Planning
Andreas E. Kyprianou, Aaron Pim, Tristan Pryer
https://arxiv.org/abs/2508.10596 https://

A Unified Framework from Boltzmann Transport to Proton Treatment Planning
This work develops a rigorous mathematical formulation of proton transport by integrating both deterministic and stochastic perspectives. The deterministic framework is based on the Boltzmann-Fokker-Planck equation, formulated as an operator equation in a suitable functional setting. The stochastic approach models proton evolution via a track-length parameterised diffusion process, whose infinitesimal generator provides an alternative description of transport. A key result is the duality betw…

Considering my options to evacuate from Vilnius if needed: via Suwalki, Klaipėda or Rīga.
https://www.lrt.lt/en/news-in-english/19/2589799/baltic-states-to-develop-joint-mass-evacuation-plans

Sampling-Based Planning Under STL Specifications: A Forward Invariance Approach
Gregorio Marchesini, Siyuan Liu, Lars Lindemann, Dimos V. Dimarogonas
https://arxiv.org/abs/2506.10739

Sampling-Based Planning Under STL Specifications: A Forward Invariance Approach
We propose a variant of the Rapidly Exploring Random Tree Star (RRT$^{\star}$) algorithm to synthesize trajectories satisfying a given spatio-temporal specification expressed in a fragment of Signal Temporal Logic (STL) for linear systems. Previous approaches for planning trajectories under STL specifications using sampling-based methods leverage either mixed-integer or non-smooth optimization techniques, with poor scalability in the horizon and complexity of the task. We adopt instead a contro…

Performance of GPT-5 in Brain Tumor MRI Reasoning
Mojtaba Safari, Shansong Wang, Mingzhe Hu, Zach Eidex, Qiang Li, Xiaofeng Yang
https://arxiv.org/abs/2508.10865 https://…

Performance of GPT-5 in Brain Tumor MRI Reasoning
Accurate differentiation of brain tumor types on magnetic resonance imaging (MRI) is critical for guiding treatment planning in neuro-oncology. Recent advances in large language models (LLMs) have enabled visual question answering (VQA) approaches that integrate image interpretation with natural language reasoning. In this study, we evaluated GPT-4o, GPT-5-nano, GPT-5-mini, and GPT-5 on a curated brain tumor VQA benchmark derived from 3 Brain Tumor Segmentation (BraTS) datasets - glioblastoma (…

Former Super Bowl MVP drawing interest from three teams in free agency: Possible landing spots for Von Miller

https://www.cbssports.com/nfl/news/former-…

How attention simplifies mental representations for planning
Jason da Silva Castanheira, Nicholas Shea, Stephen M. Fleming
https://arxiv.org/abs/2506.09520

How attention simplifies mental representations for planning
Human planning is efficient -- it frugally deploys limited cognitive resources to accomplish difficult tasks -- and flexible -- adapting to novel problems and environments. Computational approaches suggest that people construct simplified mental representations of their environment, balancing the complexity of a task representation with its utility. These models imply a nested optimisation in which planning shapes perception, and perception shapes planning -- but the perceptual and attentional …

Just scrolling through the "Tree Preservation Orders" MHCLG has published geo-data for.
https://www.planning.data.gov.uk/dataset/tree

Repairing General Game Descriptions (extended version)
Yifan He, Munyque Mittelmann, Aniello Murano, Abdallah Saffidine, Michael Thielscher
https://arxiv.org/abs/2508.10438 http…

Repairing General Game Descriptions (extended version)
The Game Description Language (GDL) is a widely used formalism for specifying the rules of general games. Writing correct GDL descriptions can be challenging, especially for non-experts. Automated theorem proving has been proposed to assist game design by verifying if a GDL description satisfies desirable logical properties. However, when a description is proved to be faulty, the repair task itself can only be done manually. Motivated by the work on repairing unsolvable planning domain descript…

Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planning
Sahil Bansal, Sai Shruthi Sistla, Aarti Arikatala, Sebastian Schreiber
https://arxiv.org/abs/2508.05888

Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planning
Effective tool retrieval is essential for AI agents to select from a vast array of tools when identifying and planning actions in the context of complex user queries. Despite its central role in planning, this aspect remains underexplored in the literature. Traditional approaches rely primarily on similarities between user queries and tool descriptions, which significantly limits retrieval accuracy, specifically when handling multi-step user requests. To address these limitations, we propose a …

Dynamic Less-Than-Truckload Transportation Planning in Hyperconnected Hub Networks with Multi-Carrier Operations
Tiankuo Zhang, Jingze Li, Benoit Montreuil
https://arxiv.org/abs/2506.10290

Dynamic Less-Than-Truckload Transportation Planning in Hyperconnected Hub Networks with Multi-Carrier Operations
Less-than-truckload (LTL) shipment is vital in modern freight transportation yet is in dire need of more efficient usage of resources, higher service responsiveness and velocity, lower overall shipping cost across all parties, and better quality of life for the drivers. The industry is currently highly fragmented, with numerous small to medium-sized LTL carriers typically operating within dedicated regions or corridors, mostly disconnected from each other. This paper investigates the large-scal…

Minneapolis and Saint Paul are finally launching this BRT line that was already in the planning phase when I last visited in ... 2019.
Good, but if it's gonna take that long, maybe agencies should just incrementally upgrade the buses that exist instead of these big fancy unicorn "BRT" projects?
https://

Creating Buzz for the B Line
The Twin Cities’ newest BRT line is an attractive option that could turn more people into transit users — if they know about it!

Data-Efficient Learning for Generalizable Surgical Video Understanding
Sahar Nasirihaghighi
https://arxiv.org/abs/2508.10215 https://arxiv.org/pdf/2508.102…

Data-Efficient Learning for Generalizable Surgical Video Understanding
Advances in surgical video analysis are transforming operating rooms into intelligent, data-driven environments. Computer-assisted systems support full surgical workflow, from preoperative planning to intraoperative guidance and postoperative assessment. However, developing robust and generalizable models for surgical video understanding remains challenging due to (I) annotation scarcity, (II) spatiotemporal complexity, and (III) domain gap across procedures and institutions. This doctoral rese…

personally i'm not planning to migrate from github any time soon. the people who i'd've lost on ethical grounds have self-selected out long ago, i have basically no lock-in besides very heavy reliance GH Actions (which have no viable replacement besides gitlab maybe) and i'm counting on the process of corporate death to take a few years until it starts to cause actual immediate problems

Computing optimal trajectories for a tethered pursuer
Aurelio Barrera-Vicent, Jos\'e Miguel D\'iaz-B\'a\~nez, Fabio Rodr\'iguez, Vanesa S\'anchez-Canales
https://arxiv.org/abs/2507.08724

Computing optimal trajectories for a tethered pursuer
In this paper, we introduce a trajectory planning problem for a marsupial robotics system consisting of a ground robot, a drone, and a taut tether of bounded length connecting the two robots. This problem can be framed within the context of a pursuit-evasion game. Using a geometric modeling approach, we present an optimal algorithm to compute a minimum-link path for the pursuer (ground robot), given the known path of the evader (drone). Furthermore, we address and solve three related geometric …

I have been trialling Substack as a supplement to my blog. Substack has a large community of people interested in local and social history, so it's a good fit for me. I'm not planning to stop blogging, and some posts may appear in both places, but it allows me scope to write on a wider range of topics. https://

Newspapers and Research
The Norwood Review and Sir Archur Conan Doyle

"Pendant des décennies, beaucoup Š gauche ont renoncé Š parler de planification économique en raison de son association avec la bureaucratie soviétique. Mais la crise climatique et la réalité de l'intervention massive de l'État dans les économies capitalistes ont rendu la planification démocratique incontournable."

For various reasons I'm probably not going to be at any Saturday protests for long, but I'm thinking about buying & dropping off some supplies to support them:
- Bottled water and/or electrolyte drinks.
- Slats, posterboard, markers, and stapler to let people who show up without a sign make their own.
Anyone else got good ideas or planning on doing supply runs?
#Protests #NoKings

#Brightline , the only privately run inter-city rail route in the United States that keeps getting handouts from government is in bad financial position.
They mainly offer service in Florida right now but are planning extension in California/Nevada
Turns out private rail might be hard to make money from, what a surprise.

Access graph: a novel graph representation of public transport networks for accessibility analysis
Tina \v{S}filigoj, Aljo\v{s}a Peperko, Oded Cats
https://arxiv.org/abs/2507.08361

Access graph: a novel graph representation of public transport networks for accessibility analysis
Accessibility, defined as travel impedance between spatially dispersed opportunities for activity, is one of the main determinants of public transport (PT) use. In-depth understanding of its properties is crucial for optimal public transport systems planning and design. Although the concept has been around for decades and there is a large body of literature on accessibility operationalisation and measurement, a unified approach is lacking. To this end, we introduce a novel graph representation …

Head down for a day of coaching, project planning - and a webinar at 1pm all about #coaching and #wellbeing. If you'd like to join (it's free!) here's the Zoom link:

LSDTs: LLM-Augmented Semantic Digital Twins for Adaptive Knowledge-Intensive Infrastructure Planning
Naiyi Li, Zihui Ma, Runlong Yu, Lingyao Li
https://arxiv.org/abs/2508.06799 …

LSDTs: LLM-Augmented Semantic Digital Twins for Adaptive Knowledge-Intensive Infrastructure Planning
Digital Twins (DTs) offer powerful tools for managing complex infrastructure systems, but their effectiveness is often limited by challenges in integrating unstructured knowledge. Recent advances in Large Language Models (LLMs) bring new potential to address this gap, with strong abilities in extracting and organizing diverse textual information. We therefore propose LSDTs (LLM-Augmented Semantic Digital Twins), a framework that helps LLMs extract planning knowledge from unstructured documents …

Rapid cardiac activation prediction for cardiac resynchronization therapy planning using geometric deep learning
Ehsan Naghavi, Haifeng Wang, Vahid Ziaei Rad, Julius Guccione, Ghassan Kassab, Vishnu Boddeti, Seungik Baek, Lik-Chuan Lee
https://arxiv.org/abs/2506.08987

Rapid cardiac activation prediction for cardiac resynchronization therapy planning using geometric deep learning
Cardiac resynchronization therapy (CRT) is a common intervention for patients with dyssynchronous heart failure, yet approximately one-third of recipients fail to respond due to suboptimal lead placement. Identifying optimal pacing sites remains challenging, largely due to patient-specific anatomical variability and the limitations of current individualized planning strategies. In a step towards constructing an in-silico approach to help address this issue, we develop two geometric deep learnin…

DT4PCP: A Digital Twin Framework for Personalized Care Planning Applied to Type 2 Diabetes Management
Javad M Alizadeh, Mukesh K Patel, Huanmei Wu
https://arxiv.org/abs/2507.07809

DT4PCP: A Digital Twin Framework for Personalized Care Planning Applied to Type 2 Diabetes Management
Digital Twin (DT) technology has emerged as a transformative approach in healthcare, but its application in personalized patient care remains limited. This paper aims to present a practical implementation of DT in the management of chronic diseases. We introduce a general DT framework for personalized care planning (DT4PCP), with the core components being a real-time virtual representation of a patient's health and emerging predictive models to enable adaptive, personalized care. We implemented…

Niners LT Trent Williams on goal of playing till he’s 40: ‘Not going to retire with something left on the table’ https://www.nfl.com/news/niners-lt-trent-williams-on-goal-of-playing-till-he-s-40-not-g…

#Agile Development was invented in the Soviet Union.

Neun sekunden warten und dann gephished werden.
#phishing

Neun sekunden warten und dann gephished werden.
#phishing

Neuro-Symbolic Acceleration of MILP Motion Planning with Temporal Logic and Chance Constraints
Junyang Cai, Weimin Huang, Jyotirmoy V. Deshmukh, Lars Lindemann, Bistra Dilkina
https://arxiv.org/abs/2508.07515

Neuro-Symbolic Acceleration of MILP Motion Planning with Temporal Logic and Chance Constraints
Autonomous systems must solve motion planning problems subject to increasingly complex, time-sensitive, and uncertain missions. These problems often involve high-level task specifications, such as temporal logic or chance constraints, which require solving large-scale Mixed-Integer Linear Programs (MILPs). However, existing MILP-based planning methods suffer from high computational cost and limited scalability, hindering their real-time applicability. We propose to use a neuro-symbolic approach…

Nearly 100 House Democrats urge RFK Jr. to restore millions in family planning grants (Nathaniel Weixel/The Hill)
https://thehill.com/policy/healthcare/5337240-lawmakers-robert-f-kennedy-jr-restoration-family-planning-grants/
http://www.memeorandum.com/250606/p107#a250606p107

I'm currently planning my #GitHubExit and I am wondering what the best option is to make my repos still discoverable but not in the hands of Microsoft (at least from now on).
A) Should I just add a hint to the readme and archive the repo?
B) Delete the git history and just add one commit with the new URL in the readme?
C)Delete the whole repository?
D) something different…

Upcoming special issue in Urban Planning (cogitatio): Citizen-Centric Urban Planning Through Participatory Sensing Data and Digital Tools
https://www.cogitatiopress.com/urbanplanning/issue/futureissues#i530
Abstract submission deadline in December.

Future Issues | Urban Planning
Urban Planning is a new international peer-reviewed open access journal of urban studies

While I can see the designs for immediate depth and future transition planning for a post VVD world (😱), what I can’t figure out about the pursuit of Guehi and/or Leoni is what that means for Konaté’s contract negotiations and future.
I’ve read that an upgraded offer was put before him in late July. #LFC absolutely cannot allow him to leave on a free next summer & Real Madrid have already exp…

Integrated Bus Fleet Electrification Planning Through Accelerated Logic-Based Benders Decomposition and Restriction Heuristics
Robin Legault, Filipe Cabral, Xu Andy Sun
https://arxiv.org/abs/2508.05863

Integrated Bus Fleet Electrification Planning Through Accelerated Logic-Based Benders Decomposition and Restriction Heuristics
To meet sustainability goals and regulatory requirements, transit agencies worldwide are planning partial and complete transitions to electric bus fleets. This paper presents the first comprehensive and computationally efficient multi-period optimization framework integrating the key planning decisions necessary to support such electrification initiatives. Our model, formulated as a two-stage integer program with integer subproblems, jointly optimizes yearly fleet and charging infrastructure in…

EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making
Yang Cheng, Zilai Wang, Weiyu Ma, Wenhui Zhu, Yue Deng, Jian Zhao
https://arxiv.org/abs/2508.09586

EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-making
Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, including programming, planning, and decision-making. However, their performance often degrades when faced with highly complex problem instances that require deep reasoning over long horizons. In such cases, direct problem-solving approaches can lead to inefficiency or failure due to the lack of structured intermediate guidance. To address this, we propose a novel self-evolve framework, EvoCurr, in wh…

Leveraging LLMs for Mission Planning in Precision Agriculture
Marcos Abel Zuzu\'arregui, Stefano Carpin
https://arxiv.org/abs/2506.10093 https://

Leveraging LLMs for Mission Planning in Precision Agriculture
Robotics and artificial intelligence hold significant potential for advancing precision agriculture. While robotic systems have been successfully deployed for various tasks, adapting them to perform diverse missions remains challenging, particularly because end users often lack technical expertise. In this paper, we present an end-to-end system that leverages large language models (LLMs), specifically ChatGPT, to enable users to assign complex data collection tasks to autonomous robots using na…

HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting
Zineddine Bettouche, Khalid Ali, Andreas Fischer, Andreas Kassler
https://arxiv.org/abs/2508.09184 http…

HiSTM: Hierarchical Spatiotemporal Mamba for Cellular Traffic Forecasting
Cellular traffic forecasting is essential for network planning, resource allocation, or load-balancing traffic across cells. However, accurate forecasting is difficult due to intricate spatial and temporal patterns that exist due to the mobility of users. Existing AI-based traffic forecasting models often trade-off accuracy and computational efficiency. We present Hierarchical SpatioTemporal Mamba (HiSTM), which combines a dual spatial encoder with a Mamba-based temporal module and attention me…

Winnipeg-based Conquest Planning, which uses AI to help financial advisors and other clients make decisions, raised an $80M Series B led by Goldman Sachs (Jon Fingas/BetaKit)
https://betakit.com/conquest-planning-

Conquest Planning raises $110-million Series B to fuel US expansion of its financial management platform | BetaKit
Growth Equity at Goldman Sachs Alternatives led the Winnipeg startup’s latest funding round.

Even outside the digital transformation of "planning", I'm very interested (as a member of the public) in the development of a rich corpus of public geospatial data. (Some deep unexpressed cartographic instinct?) This blogpost is an update on the status of that:
https://…

Standardising the planning application process: Progress and next steps – MHCLG Digital
The blog of the digital and data teams in MHCLG

The Trump regime's coordinated repression did not come from nowhere.
In 2024, TheNew York Times reported that the Trump transition team was already planning to use emergency powers, such as the one Trump invoked on June 7,
to confront political opponents who protest the administration.
While the Trump administration planned this move for about a year,
the deployment of troops in Los Angeles is the brutal culmination of a yearslong campaign to systematically erod…

Military Crackdown on LA Protests Portends Further Erosion of Everyone’s Rights
Governments at all levels from both parties have paved the way to an authoritarian response to dissent.

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/9]:
- QuickGrasp: Lightweight Antipodal Grasp Planning with Point Clouds
Navin Sriram Ravie, Keerthi Vasan M, Asokan Thondiyath, Bijo Sebastian

One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture
Marcos Abel Zuzu\'arregui, Mustafa Melih Toslak, Stefano Carpin
https://arxiv.org/abs/2506.10106

One For All: LLM-based Heterogeneous Mission Planning in Precision Agriculture
Artificial intelligence is transforming precision agriculture, offering farmers new tools to streamline their daily operations. While these technological advances promise increased efficiency, they often introduce additional complexity and steep learning curves that are particularly challenging for non-technical users who must balance tech adoption with existing workloads. In this paper, we present a natural language (NL) robotic mission planner that enables non-specialists to control heterogen…

Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation
Xunzhi Xiang, Yabo Chen, Guiyu Zhang, Zhongyu Wang, Zhe Gao, Quanming Xiang, Gonghu Shang, Junqi Liu, Haibin Huang, Yang Gao, Chi Zhang, Qi Fan, Xuelong Li
https://arxiv.org/abs/2508.03334

Macro-from-Micro Planning for High-Quality and Parallelized Autoregressive Long Video Generation
Current autoregressive diffusion models excel at video generation but are generally limited to short temporal durations. Our theoretical analysis indicates that the autoregressive modeling typically suffers from temporal drift caused by error accumulation and hinders parallelization in long video synthesis. To address these limitations, we propose a novel planning-then-populating framework centered on Macro-from-Micro Planning (MMPL) for long video generation. MMPL sketches a global storyline f…

BESS Participation Planning for Provision of Grid Services in Energy and Regulation Markets
Zeenat Hameed, Chresten Traeholt
https://arxiv.org/abs/2506.08472

BESS Participation Planning for Provision of Grid Services in Energy and Regulation Markets
This paper presents a stochastic optimization model for planning the participation of battery energy storage systems (BESSs) in energy and regulation markets. The proposed model quantifies and compares the business value of single and multi-market BESS services by accounting for their bid submission and acceptance procedures, pricing mechanisms and revenue streams, and penalty payments and degradation costs. By modelling the price uncertainties using market price scenarios (MPSs) and considerin…

Large Scale Robotic Material Handling: Learning, Planning, and Control
Filippo A. Spinelli, Yifan Zhai, Fang Nan, Pascal Egli, Julian Nubert, Thilo Bleumer, Lukas Miller, Ferdinand Hofmann, Marco Hutter
https://arxiv.org/abs/2508.09003

Large Scale Robotic Material Handling: Learning, Planning, and Control
Bulk material handling involves the efficient and precise moving of large quantities of materials, a core operation in many industries, including cargo ship unloading, waste sorting, construction, and demolition. These repetitive, labor-intensive, and safety-critical operations are typically performed using large hydraulic material handlers equipped with underactuated grippers. In this work, we present a comprehensive framework for the autonomous execution of large-scale material handling tasks…

Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
Licong Xu, Milind Sarkar, Anto I. Lonappan, \'I\~nigo Zubeldia, Pablo Villanueva-Domingo, Santiago Casas, Christian Fidler, Chetana Amancharla, Ujjwal Tiwari, Adrian Bayer, Chadi Ait Ekiou, Miles Cranmer, Adrian Dimitrov, James Fergusson, Kahaan Gandhi, Sven Krippendorf, Andrew Laverick, Julien Lesgourgues, Antony Lewis, Thomas Meier, Blake Sherwin, Kristen Surrao, Francisco Villaescu…

Open Source Planning & Control System with Language Agents for Autonomous Scientific Discovery
We present a multi-agent system for automation of scientific research tasks, cmbagent. The system is formed by about 30 Large Language Model (LLM) agents and implements a Planning & Control strategy to orchestrate the agentic workflow, with no human-in-the-loop at any point. Each agent specializes in a different task (performing retrieval on scientific papers and codebases, writing code, interpreting results, critiquing the output of other agents) and the system is able to execute code locally.…

From Theory to Practice: Advancing Multi-Robot Path Planning Algorithms and Applications
Teng Guo
https://arxiv.org/abs/2506.09914 https://

From Theory to Practice: Advancing Multi-Robot Path Planning Algorithms and Applications
The labeled MRPP (Multi-Robot Path Planning) problem involves routing robots from start to goal configurations efficiently while avoiding collisions. Despite progress in solution quality and runtime, its complexity and industrial relevance continue to drive research. This dissertation introduces scalable MRPP methods with provable guarantees and practical heuristics. First, we study dense MRPP on 2D grids, relevant to warehouse and parcel systems. We propose the Rubik Table method, achieving …

A Multi-Model Probabilistic Framework for Seismic Risk Assessment and Retrofit Planning of Electric Power Networks
Huangbin Liang, Beatriz Moya, Francisco Chinesta, Eleni Chatzi
https://arxiv.org/abs/2508.07376

A Multi-Model Probabilistic Framework for Seismic Risk Assessment and Retrofit Planning of Electric Power Networks
Electric power networks are critical lifelines, and their disruption during earthquakes can lead to severe cascading failures and significantly hinder post-disaster recovery. Enhancing their seismic resilience requires identifying and strengthening vulnerable components in a cost-effective and system-aware manner. However, existing studies often overlook the systemic behavior of power networks under seismic loading. Common limitations include isolated component analyses that neglect network-wid…

Linear, which makes software development and project planning tools that rival Atlassian's Jira, raised an $82M Series C led by Accel, valuing it at $1.25B (Anna Tong/Reuters)
https://www.reuters.com/business/atlassian

A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning
Zihang Qiu, Ali Ajdari, Mislav Bobi\'c, Thomas Bortfeld, Dick den Hertog, Jannis Kurtz, Hoyeon Lee
https://arxiv.org/abs/2508.07368

A K-adaptability Approach to Proton Radiation Therapy Robust Treatment Planning
Uncertainties such as setup and range errors can significantly compromise proton therapy. A discrete uncertainty set is often constructed to represent different uncertainty scenarios. A min-max robust optimization approach is then utilized to optimize the worst-case performance of a radiation therapy plan against the uncertainty set. However, the min-max approach can be too conservative as a single plan has to account for the entire uncertainty set. K-adaptability is a novel approach to robust …

Situationally-aware Path Planning Exploiting 3D Scene Graphs
Saad Ejaz, Marco Giberna, Muhammad Shaheer, Jose Andres Millan-Romera, Ali Tourani, Paul Kremer, Holger Voos, Jose Luis Sanchez-Lopez
https://arxiv.org/abs/2508.06283

Situationally-aware Path Planning Exploiting 3D Scene Graphs
3D Scene Graphs integrate both metric and semantic information, yet their structure remains underutilized for improving path planning efficiency and interpretability. In this work, we present S-Path, a situationally-aware path planner that leverages the metric-semantic structure of indoor 3D Scene Graphs to significantly enhance planning efficiency. S-Path follows a two-stage process: it first performs a search over a semantic graph derived from the scene graph to yield a human-understandable h…

Planning with Dynamically Changing Domains
Mikhail Soutchanski, Yongmei Liu
https://arxiv.org/abs/2508.02697 https://arxiv.org/pdf/2508.02697

Planning with Dynamically Changing Domains
In classical planning and conformant planning, it is assumed that there are finitely many named objects given in advance, and only they can participate in actions and in fluents. This is the Domain Closure Assumption (DCA). However, there are practical planning problems where the set of objects changes dynamically as actions are performed; e.g., new objects can be created, old objects can be destroyed. We formulate the planning problem in first-order logic, assume an initial theory is a finite …

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
Ne\c{s}et \"Unver Akmandor, Sarvesh Prajapati, Mark Zolotas, Ta\c{s}k{\i}n Pad{\i}r
https://arxiv.org/abs/2506.08344

Re4MPC: Reactive Nonlinear MPC for Multi-model Motion Planning via Deep Reinforcement Learning
Traditional motion planning methods for robots with many degrees-of-freedom, such as mobile manipulators, are often computationally prohibitive for real-world settings. In this paper, we propose a novel multi-model motion planning pipeline, termed Re4MPC, which computes trajectories using Nonlinear Model Predictive Control (NMPC). Re4MPC generates trajectories in a computationally efficient manner by reactively selecting the model, cost, and constraints of the NMPC problem depending on the comp…

Dynamical Trajectory Planning of Disturbance Consciousness for Air-Land Bimodal Unmanned Aerial Vehicles
Shaoting Liu, Zhou Liu
https://arxiv.org/abs/2508.05972 https://

Dynamical Trajectory Planning of Disturbance Consciousness for Air-Land Bimodal Unmanned Aerial Vehicles
Air-land bimodal vehicles provide a promising solution for navigating complex environments by combining the flexibility of aerial locomotion with the energy efficiency of ground mobility. To enhance the robustness of trajectory planning under environmental disturbances, this paper presents a disturbance-aware planning framework that incorporates real-time disturbance estimation into both path searching and trajectory optimization. A key component of the framework is a disturbance-adaptive safet…

A Learning-Based Framework for Collision-Free Motion Planning
Mateus Salom\~ao, Tiany\"u Ren, Alexander K\"onig
https://arxiv.org/abs/2508.07502 https://

A Learning-Based Framework for Collision-Free Motion Planning
This paper presents a learning-based extension to a Circular Field (CF)-based motion planner for efficient, collision-free trajectory generation in cluttered environments. The proposed approach overcomes the limitations of hand-tuned force field parameters by employing a deep neural network trained to infer optimal planner gains from a single depth image of the scene. The pipeline incorporates a CUDA-accelerated perception module, a predictive agent-based planning strategy, and a dataset genera…

PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly
Liang Ma, Jiajun Wen, Min Lin, Rongtao Xu, Xiwen Liang, Bingqian Lin, Jun Ma, Yongxin Wang, Ziming Wei, Haokun Lin, Mingfei Han, Meng Cao, Bokui Chen, Ivan Laptev, Xiaodan Liang
https://arxiv.org/abs/2506.08708

PhyBlock: A Progressive Benchmark for Physical Understanding and Planning via 3D Block Assembly
While vision-language models (VLMs) have demonstrated promising capabilities in reasoning and planning for embodied agents, their ability to comprehend physical phenomena, particularly within structured 3D environments, remains severely limited. To close this gap, we introduce PhyBlock, a progressive benchmark designed to assess VLMs on physical understanding and planning through robotic 3D block assembly tasks. PhyBlock integrates a novel four-level cognitive hierarchy assembly task alongside …

Application of LLMs to Multi-Robot Path Planning and Task Allocation
Ashish Kumar
https://arxiv.org/abs/2507.07302 https://arxiv.org/…

Application of LLMs to Multi-Robot Path Planning and Task Allocation
Efficient exploration is a well known problem in deep reinforcement learning and this problem is exacerbated in multi-agent reinforcement learning due the intrinsic complexities of such algorithms. There are several approaches to efficiently explore an environment to learn to solve tasks by multi-agent operating in that environment, of which, the idea of expert exploration is investigated in this work. More specifically, this work investigates the application of large-language models as expert …

Towards Safe Autonomous Driving: A Real-Time Safeguarding Concept for Motion Planning Algorithms
Korbinian Moller, Rafael Neher, Marvin Seegert, Johannes Betz
https://arxiv.org/abs/2507.07444

Towards Safe Autonomous Driving: A Real-Time Safeguarding Concept for Motion Planning Algorithms
Ensuring the functional safety of motion planning modules in autonomous vehicles remains a critical challenge, especially when dealing with complex or learning-based software. Online verification has emerged as a promising approach to monitor such systems at runtime, yet its integration into embedded real-time environments remains limited. This work presents a safeguarding concept for motion planning that extends prior approaches by introducing a time safeguard. While existing methods focus on …

Incremental Language Understanding for Online Motion Planning of Robot Manipulators
Mitchell Abrams, Thies Oelerich, Christian Hartl-Nesic, Andreas Kugi, Matthias Scheutz
https://arxiv.org/abs/2508.06095

Incremental Language Understanding for Online Motion Planning of Robot Manipulators
Human-robot interaction requires robots to process language incrementally, adapting their actions in real-time based on evolving speech input. Existing approaches to language-guided robot motion planning typically assume fully specified instructions, resulting in inefficient stop-and-replan behavior when corrections or clarifications occur. In this paper, we introduce a novel reasoning-based incremental parser which integrates an online motion planning algorithm within the cognitive architectur…

Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning
Chan Young Park, Jillian Fisher, Marius Memmel, Dipika Khullar, Andy Yun, Abhishek Gupta, Yejin Choi
https://arxiv.org/abs/2507.08224

Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning
Large language models (LLMs) have shown promise in robotic procedural planning, yet their human-centric reasoning often omits the low-level, grounded details needed for robotic execution. Vision-language models (VLMs) offer a path toward more perceptually grounded plans, but current methods either rely on expensive, large-scale models or are constrained to narrow simulation settings. We introduce SelfReVision, a lightweight and scalable self-improvement framework for vision-language procedural …

Forecast-Driven MPC for Decentralized Multi-Robot Collision Avoidance
Hadush Hailu, Bruk Gebregziabher, Prudhvi Raj
https://arxiv.org/abs/2508.08264 https://

Forecast-Driven MPC for Decentralized Multi-Robot Collision Avoidance
The Iterative Forecast Planner (IFP) is a geometric planning approach that offers lightweight computations, scal- able, and reactive solutions for multi-robot path planning in decentralized, communication-free settings. However, it struggles in symmetric configurations, where mirrored interactions often lead to collisions and deadlocks. We introduce eIFP-MPC, an optimized and extended version of IFP that improves robustness and path consistency in dense, dynamic environments. The method refines…

DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation
Haoxiang Shi, Xiang Deng, Zaijing Li, Gongwei Chen, Yaowei Wang, Liqiang Nie
https://arxiv.org/abs/2508.09444

DAgger Diffusion Navigation: DAgger Boosted Diffusion Policy for Vision-Language Navigation
Vision-Language Navigation in Continuous Environments (VLN-CE) requires agents to follow natural language instructions through free-form 3D spaces. Existing VLN-CE approaches typically use a two-stage waypoint planning framework, where a high-level waypoint predictor generates the navigable waypoints, and then a navigation planner suggests the intermediate goals in the high-level action space. However, this two-stage decomposition framework suffers from: (1) global sub-optimization due to the p…

Humanoid Robot Acrobatics Utilizing Complete Articulated Rigid Body Dynamics
Gerald Brantner
https://arxiv.org/abs/2508.08258 https://arxiv.org/pdf/2508.08…

Humanoid Robot Acrobatics Utilizing Complete Articulated Rigid Body Dynamics
Endowing humanoid robots with the ability to perform highly dynamic motions akin to human-level acrobatics has been a long-standing challenge. Successfully performing these maneuvers requires close consideration of the underlying physics in both trajectory optimization for planning and control during execution. This is particularly challenging due to humanoids' high degree-of-freedom count and associated exponentially scaling complexities, which makes planning on the explicit equations of motio…

Deep Reinforcement Learning-Based Motion Planning and PDE Control for Flexible Manipulators
Amir Hossein Barjini, Seyed Adel Alizadeh Kolagar, Sadeq Yaqubi, Jouni Mattila
https://arxiv.org/abs/2506.08639

Deep Reinforcement Learning-Based Motion Planning and PDE Control for Flexible Manipulators
This article presents a motion planning and control framework for flexible robotic manipulators, integrating deep reinforcement learning (DRL) with a nonlinear partial differential equation (PDE) controller. Unlike conventional approaches that focus solely on control, we demonstrate that the desired trajectory significantly influences endpoint vibrations. To address this, a DRL motion planner, trained using the soft actor-critic (SAC) algorithm, generates optimized trajectories that inherently …

Attention-based Learning for 3D Informative Path Planning
Rui Zhao, Xingjian Zhang, Yuhong Cao, Yizhuo Wang, Guillaume Sartoretti
https://arxiv.org/abs/2506.08434

Attention-based Learning for 3D Informative Path Planning
In this work, we propose an attention-based deep reinforcement learning approach to address the adaptive informative path planning (IPP) problem in 3D space, where an aerial robot equipped with a downward-facing sensor must dynamically adjust its 3D position to balance sensing footprint and accuracy, and finally obtain a high-quality belief of an underlying field of interest over a given domain (e.g., presence of specific plants, hazardous gas, geological structures, etc.). In adaptive IPP task…

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning
Giwon Lee, Wooseong Jeong, Daehee Park, Jaewoo Jeong, Kuk-Jin Yoon
https://arxiv.org/abs/2507.04790

Interaction-Merged Motion Planning: Effectively Leveraging Diverse Motion Datasets for Robust Planning
Motion planning is a crucial component of autonomous robot driving. While various trajectory datasets exist, effectively utilizing them for a target domain remains challenging due to differences in agent interactions and environmental characteristics. Conventional approaches, such as domain adaptation or ensemble learning, leverage multiple source datasets but suffer from domain imbalance, catastrophic forgetting, and high computational costs. To address these challenges, we propose Interaction…

Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning
Mingyue Lei, Zewei Zhou, Hongchen Li, Jiaqi Ma, Jia Hu
https://arxiv.org/abs/2508.07686

Risk Map As Middleware: Towards Interpretable Cooperative End-to-end Autonomous Driving for Risk-Aware Planning
End-to-end paradigm has emerged as a promising approach to autonomous driving. However, existing single-agent end-to-end pipelines are often constrained by occlusion and limited perception range, resulting in hazardous driving. Furthermore, their black-box nature prevents the interpretability of the driving behavior, leading to an untrustworthiness system. To address these limitations, we introduce Risk Map as Middleware (RiskMM) and propose an interpretable cooperative end-to-end driving frame…

Attention-Based Map Encoding for Learning Generalized Legged Locomotion
Junzhe He, Chong Zhang, Fabian Jenelten, Ruben Grandia, Moritz B\"Acher, Marco Hutter
https://arxiv.org/abs/2506.09588

Attention-Based Map Encoding for Learning Generalized Legged Locomotion
Dynamic locomotion of legged robots is a critical yet challenging topic in expanding the operational range of mobile robots. It requires precise planning when possible footholds are sparse, robustness against uncertainties and disturbances, and generalizability across diverse terrains. While traditional model-based controllers excel at planning on complex terrains, they struggle with real-world uncertainties. Learning-based controllers offer robustness to such uncertainties but often lack preci…

Capsizing-Guided Trajectory Optimization for Autonomous Navigation with Rough Terrain
Wei Zhang, Yinchuan Wang, Wangtao Lu, Pengyu Zhang, Xiang Zhang, Yue Wang, Chaoqun Wang
https://arxiv.org/abs/2508.08108

Capsizing-Guided Trajectory Optimization for Autonomous Navigation with Rough Terrain
It is a challenging task for ground robots to autonomously navigate in harsh environments due to the presence of non-trivial obstacles and uneven terrain. This requires trajectory planning that balances safety and efficiency. The primary challenge is to generate a feasible trajectory that prevents robot from tip-over while ensuring effective navigation. In this paper, we propose a capsizing-aware trajectory planner (CAP) to achieve trajectory planning on the uneven terrain. The tip-over stabili…

Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition
Wule Mao, Zhouheng Li, Yunhao Luo, Yilun Du, Lei Xie
https://arxiv.org/abs/2507.04384

Rapid and Safe Trajectory Planning over Diverse Scenes through Diffusion Composition
Safe trajectory planning remains a significant challenge in complex environments, where traditional methods often trade off computational efficiency for safety. Comprehensive obstacle modeling improves safety but is computationally expensive, while approximate methods are more efficient but may compromise safety. To address this issue, this paper introduces a rapid and safe trajectory planning framework based on state-based diffusion models. Leveraging only low-dimensional vehicle states, the d…

Learning to See and Act: Task-Aware View Planning for Robotic Manipulation
Yongjie Bai, Zhouxia Wang, Yang Liu, Weixing Chen, Ziliang Chen, Mingtong Dai, Yongsen Zheng, Lingbo Liu, Guanbin Li, Liang Lin
https://arxiv.org/abs/2508.05186

Learning to See and Act: Task-Aware View Planning for Robotic Manipulation
Recent vision-language-action (VLA) models for multi-task robotic manipulation commonly rely on static viewpoints and shared visual encoders, which limit 3D perception and cause task interference, hindering robustness and generalization. In this work, we propose Task-Aware View Planning (TAVP), a framework designed to overcome these challenges by integrating active view planning with task-specific representation learning. TAVP employs an efficient exploration policy, accelerated by a novel pseu…

V*: An Efficient Motion Planning Algorithm for Autonomous Vehicles
Abdullah Zareh Andaryan, Michael G. H. Bell, Mohsen Ramezani, Glenn Geers
https://arxiv.org/abs/2508.06404 htt…

V*: An Efficient Motion Planning Algorithm for Autonomous Vehicles
Autonomous vehicle navigation in structured environments requires planners capable of generating time-optimal, collision-free trajectories that satisfy dynamic and kinematic constraints. We introduce V*, a graph-based motion planner that represents speed and direction as explicit state variables within a discretised space-time-velocity lattice. Unlike traditional methods that decouple spatial search from dynamic feasibility or rely on post-hoc smoothing, V* integrates both motion dimensions dir…

End-to-End Framework for Robot Lawnmower Coverage Path Planning using Cellular Decomposition
Nikunj Shah, Utsav Dey, Kenji Nishimiya
https://arxiv.org/abs/2506.06028

End-to-End Framework for Robot Lawnmower Coverage Path Planning using Cellular Decomposition
Efficient Coverage Path Planning (CPP) is necessary for autonomous robotic lawnmowers to effectively navigate and maintain lawns with diverse and irregular shapes. This paper introduces a comprehensive end-to-end pipeline for CPP, designed to convert user-defined boundaries on an aerial map into optimized coverage paths seamlessly. The pipeline includes user input extraction, coordinate transformation, area decomposition and path generation using our novel AdaptiveDecompositionCPP algorithm, pr…

Comparison of Path Planning Algorithms for Autonomous Vehicle Navigation Using Satellite and Airborne LiDAR Data
Chang Liu, Zhexiong Xue, Tamas Sziranyi
https://arxiv.org/abs/2507.05884

Comparison of Path Planning Algorithms for Autonomous Vehicle Navigation Using Satellite and Airborne LiDAR Data
Autonomous vehicle navigation in unstructured environments, such as forests and mountainous regions, presents significant challenges due to irregular terrain and complex road conditions. This work provides a comparative evaluation of mainstream and well-established path planning algorithms applied to weighted pixel-level road networks derived from high-resolution satellite imagery and airborne LiDAR data. For 2D road-map navigation, where the weights reflect road conditions and terrain difficul…

Industrial Robot Motion Planning with GPUs: Integration of cuRobo for Extended DOF Systems
Luai Abuelsamen, Harsh Rana, Ho-Wei Lu, Wenhan Tang, Swati Priyadarshini, Gabriel Gomes
https://arxiv.org/abs/2508.04146

Industrial Robot Motion Planning with GPUs: Integration of cuRobo for Extended DOF Systems
Efficient motion planning remains a key challenge in industrial robotics, especially for multi-axis systems operating in complex environments. This paper addresses that challenge by integrating GPU-accelerated motion planning through NVIDIA's cuRobo library into Vention's modular automation platform. By leveraging accurate CAD-based digital twins and real-time parallel optimization, our system enables rapid trajectory generation and dynamic collision avoidance for pick-and-place tasks. We demon…

Reliable and Real-Time Highway Trajectory Planning via Hybrid Learning-Optimization Frameworks
Yujia Lu, Chong Wei, Lu Ma
https://arxiv.org/abs/2508.04436 https://

Reliable and Real-Time Highway Trajectory Planning via Hybrid Learning-Optimization Frameworks
Autonomous highway driving presents a high collision risk due to fast-changing environments and limited reaction time, necessitating reliable and efficient trajectory planning. This paper proposes a hybrid trajectory planning framework that integrates the adaptability of learning-based methods with the formal safety guarantees of optimization-based approaches. The framework features a two-layer architecture: an upper layer employing a graph neural network (GNN) trained on real-world highway dat…

DeepFleet: Multi-Agent Foundation Models for Mobile Robots
Ameya Agaskar, Sriram Siva, William Pickering, Kyle O'Brien, Charles Kekeh, Ang Li, Brianna Gallo Sarker, Alicia Chua, Mayur Nemade, Charun Thattai, Jiaming Di, Isaac Iyengar, Ramya Dharoor, Dino Kirouani, Jimmy Erskine, Tamir Hegazy, Scott Niekum, Usman A. Khan, Federico Pecora, Joseph W. Durham
ht…

DeepFleet: Multi-Agent Foundation Models for Mobile Robots
We introduce DeepFleet, a suite of foundation models designed to support coordination and planning for large-scale mobile robot fleets. These models are trained on fleet movement data, including robot positions, goals, and interactions, from hundreds of thousands of robots in Amazon warehouses worldwide. DeepFleet consists of four architectures that each embody a distinct inductive bias and collectively explore key points in the design space for multi-agent foundation models: the robot-centric …

Enhanced Trust Region Sequential Convex Optimization for Multi-Drone Thermal Screening Trajectory Planning in Urban Environments
Kaiyuan Chen, Zhengjie Hu, Shaolin Zhang, Yuanqing Xia, Wannian Liang, Shuo Wang
https://arxiv.org/abs/2506.06012

Enhanced Trust Region Sequential Convex Optimization for Multi-Drone Thermal Screening Trajectory Planning in Urban Environments
The rapid detection of abnormal body temperatures in urban populations is essential for managing public health risks, especially during outbreaks of infectious diseases. Multi-drone thermal screening systems offer promising solutions for fast, large-scale, and non-intrusive human temperature monitoring. However, trajectory planning for multiple drones in complex urban environments poses significant challenges, including collision avoidance, coverage efficiency, and constrained flight environmen…

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT
Muhammad A. Muttaqien, Tomohiro Motoda, Ryo Hanai, Yukiyasu Domae
https://arxiv.org/abs/2508.08748

Visual Prompting for Robotic Manipulation with Annotation-Guided Pick-and-Place Using ACT
Robotic pick-and-place tasks in convenience stores pose challenges due to dense object arrangements, occlusions, and variations in object properties such as color, shape, size, and texture. These factors complicate trajectory planning and grasping. This paper introduces a perception-action pipeline leveraging annotation-guided visual prompting, where bounding box annotations identify both pickable objects and placement locations, providing structured spatial guidance. Instead of traditional ste…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new/
[1/1]:
EAST: Environment Aware Safe Tracking using Planning and Control Co-Design

Developing a Calibrated Physics-Based Digital Twin for Construction Vehicles
Deniz Karanfil, Daniel Lindmark, Martin Servin, David Torick, Bahram Ravani
https://arxiv.org/abs/2508.08576

Developing a Calibrated Physics-Based Digital Twin for Construction Vehicles
This paper presents the development of a calibrated digital twin of a wheel loader. A calibrated digital twin integrates a construction vehicle with a high-fidelity digital model allowing for automated diagnostics and optimization of operations as well as pre-planning simulations enhancing automation capabilities. The high-fidelity digital model is a virtual twin of the physical wheel loader. It uses a physics-based multibody dynamic model of the wheel loader in the software AGX Dynamics. Inter…

Tru-POMDP: Task Planning Under Uncertainty via Tree of Hypotheses and Open-Ended POMDPs
Wenjing Tang, Xinyu He, Yongxi Huang, Yunxiao Xiao, Cewu Lu, Panpan Cai
https://arxiv.org/abs/2506.02860

Tru-POMDP: Task Planning Under Uncertainty via Tree of Hypotheses and Open-Ended POMDPs
Task planning under uncertainty is essential for home-service robots operating in the real world. Tasks involve ambiguous human instructions, hidden or unknown object locations, and open-vocabulary object types, leading to significant open-ended uncertainty and a boundlessly large planning space. To address these challenges, we propose Tru-POMDP, a planner that combines structured belief generation using Large Language Models (LLMs) with principled POMDP planning. Tru-POMDP introduces a hierarc…