Tootfinder

Grasp-HGN: Grasping the Unexpected
Mehrshad Zandigohar, Mallesham Dasari, Gunar Schirner
https://arxiv.org/abs/2508.07648 https://arxiv.org/pdf/2508.07648

Grasp-HGN: Grasping the Unexpected
For transradial amputees, robotic prosthetic hands promise to regain the capability to perform daily living activities. To advance next-generation prosthetic hand control design, it is crucial to address current shortcomings in robustness to out of lab artifacts, and generalizability to new environments. Due to the fixed number of object to interact with in existing datasets, contrasted with the virtually infinite variety of objects encountered in the real world, current grasp models perform po…

Improving Continuous Grasp Force Decoding from EEG with Time-Frequency Regressors and Premotor-Parietal Network Integration
Parth G. Dangi, Yogesh Kumar Meena
https://arxiv.org/abs/2508.07677

Improving Continuous Grasp Force Decoding from EEG with Time-Frequency Regressors and Premotor-Parietal Network Integration
Brain-machine interfaces (BMIs) have significantly advanced neuro-rehabilitation by enhancing motor control. However, accurately decoding continuous grasp force remains a challenge, limiting the effectiveness of BMI applications for fine motor tasks. Current models tend to prioritise algorithmic complexity rather than incorporating neurophysiological insights into force control, which is essential for developing effective neural engineering solutions. To address this, we propose EEGForceMap, an…

The judicial cases in OKC are closely linked to the silence and lack of accountability of Tibetan Buddhist Heads of Nyigmapa Lineages in 🇫🇷🇧🇪🇵🇹. To grasp the full extent of responsibility and complacency, check out this documentary. #TibetanBuddhism #Responsibility

Attribute-based Object Grounding and Robot Grasp Detection with Spatial Reasoning
Houjian Yu, Zheming Zhou, Min Sun, Omid Ghasemalizadeh, Yuyin Sun, Cheng-Hao Kuo, Arnie Sen, Changhyun Choi
https://arxiv.org/abs/2509.08126

Attribute-based Object Grounding and Robot Grasp Detection with Spatial Reasoning
Enabling robots to grasp objects specified through natural language is essential for effective human-robot interaction, yet it remains a significant challenge. Existing approaches often struggle with open-form language expressions and typically assume unambiguous target objects without duplicates. Moreover, they frequently rely on costly, dense pixel-wise annotations for both object grounding and grasp configuration. We present Attribute-based Object Grounding and Robotic Grasping (OGRG), a nov…

Grasp Like Humans: Learning Generalizable Multi-Fingered Grasping from Human Proprioceptive Sensorimotor Integration
Ce Guo, Xieyuanli Chen, Zhiwen Zeng, Zirui Guo, Yihong Li, Haoran Xiao, Dewen Hu, Huimin Lu
https://arxiv.org/abs/2509.08354

Grasp Like Humans: Learning Generalizable Multi-Fingered Grasping from Human Proprioceptive Sensorimotor Integration
Tactile and kinesthetic perceptions are crucial for human dexterous manipulation, enabling reliable grasping of objects via proprioceptive sensorimotor integration. For robotic hands, even though acquiring such tactile and kinesthetic feedback is feasible, establishing a direct mapping from this sensory feedback to motor actions remains challenging. In this paper, we propose a novel glove-mediated tactile-kinematic perception-prediction framework for grasp skill transfer from human intuitive an…

DexVLG: Dexterous Vision-Language-Grasp Model at Scale
Jiawei He, Danshi Li, Xinqiang Yu, Zekun Qi, Wenyao Zhang, Jiayi Chen, Zhaoxiang Zhang, Zhizheng Zhang, Li Yi, He Wang
https://arxiv.org/abs/2507.02747

DexVLG: Dexterous Vision-Language-Grasp Model at Scale
As large models gain traction, vision-language-action (VLA) systems are enabling robots to tackle increasingly complex tasks. However, limited by the difficulty of data collection, progress has mainly focused on controlling simple gripper end-effectors. There is little research on functional grasping with large models for human-like dexterous hands. In this paper, we introduce DexVLG, a large Vision-Language-Grasp model for Dexterous grasp pose prediction aligned with language instructions usin…

Addressing the Heterogeneity of Visualization in an Introductory PhD Course in the Swedish Context
Kostiantyn Kucher, Niklas R\"onnberg, Jonas L\"owgren
https://arxiv.org/abs/2508.08958

Addressing the Heterogeneity of Visualization in an Introductory PhD Course in the Swedish Context
Visualization is a heterogeneous field, and this aspect is often reflected by the organizational structures at higher education institutions that academic researchers in visualization and related fields including computer graphics, human-computer interaction, and media design are typically affiliated with. It may thus be a challenge for new PhD students to grasp the fragmented structure of their new workplace, form collegial relations across the institution, and to build a coherent picture of t…

When you're so used to programming languages with high-level methods of checking for empty strings, that you grasp for something like `not x`, `x.empty()`, `len(x) == 0`… and only after a while of thinking, you realize that it's `x == ''`.
#Meson

Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Control
Jun Yamada, Adithyavairavan Murali, Ajay Mandlekar, Clemens Eppner, Ingmar Posner, Balakumar Sundaralingam
https://arxiv.org/abs/2509.06201

Grasp-MPC: Closed-Loop Visual Grasping via Value-Guided Model Predictive Control
Grasping of diverse objects in unstructured environments remains a significant challenge. Open-loop grasping methods, effective in controlled settings, struggle in cluttered environments. Grasp prediction errors and object pose changes during grasping are the main causes of failure. In contrast, closed-loop methods address these challenges in simplified settings (e.g., single object on a table) on a limited set of objects, with no path to generalization. We propose Grasp-MPC, a closed-loop 6-Do…

It is vital that we don't overestimate our understanding of human cognition.
Yes, there is reason to believe we have a better grasp of some things than when AI hype first appeared in the 1950s, but that better understanding is not clear, and almost certainly isn't on the basis of simple computation

Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors
Haoyu Zhao, Linghao Zhuang, Xingyue Zhao, Cheng Zeng, Haoran Xu, Yuming Jiang, Jun Cen, Kexiang Wang, Jiayan Guo, Siteng Huang, Xin Li, Deli Zhao, Hua Zou
https://arxiv.org/abs/2508.08896

Towards Affordance-Aware Robotic Dexterous Grasping with Human-like Priors
A dexterous hand capable of generalizable grasping objects is fundamental for the development of general-purpose embodied AI. However, previous methods focus narrowly on low-level grasp stability metrics, neglecting affordance-aware positioning and human-like poses which are crucial for downstream manipulation. To address these limitations, we propose AffordDex, a novel framework with two-stage training that learns a universal grasping policy with an inherent understanding of both motion priors…

A LLM-Driven Multi-Agent Systems for Professional Development of Mathematics Teachers
Kaiqi Yang, Hang Li, Yucheng Chu, Ahreum Han, Yasemin Copur-Gencturk, Jiliang Tang, Hui Liu
https://arxiv.org/abs/2507.05292

A LLM-Driven Multi-Agent Systems for Professional Development of Mathematics Teachers
Professional development (PD) serves as the cornerstone for teacher tutors to grasp content knowledge. However, providing equitable and timely PD opportunities for teachers poses significant challenges. To address this issue, we introduce I-VIP (Intelligent Virtual Interactive Program), an intelligent tutoring platform for teacher professional development, driven by large language models (LLMs) and supported by multi-agent frameworks. This platform offers a user-friendly conversational interfac…

This #wasp is cutting cat food into smaller pieces and flies away with them!😡

Replaced article(s) found for cs.HC. https://arxiv.org/list/cs.HC/new
[1/1]:
- GraspR: A Computational Model of Spatial User Preferences for Adaptive Grasp UI Design
Arthur Caetano, Yunhao Luo, Adwait Sharma, Misha Sra

"More than 10,000 species on brink of extinction need urgent action: Study"
#Animals #Extinction

More than 10,000 species on brink of extinction need urgent action: Study
Extinction is an overwhelming concept, difficult to grasp in its enormity and finality. Thousands of species are barreling toward that grim fate, unless we help. A comprehensive new study provides the clearest picture yet of Earth’s most imperiled species, and offers evidence that conservation can work. The study published in Nature Reviews Biodiversity found that […]

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/4]:
- FFHFlow: Diverse and Uncertainty-Aware Dexterous Grasp Generation via Flow Variational Inference
Qian Feng, Jianxiang Feng, Zhaopeng Chen, Rudolph Triebel, Alois Knoll

GRASP: Grouped Regression with Adaptive Shrinkage Priors
Shu Yu Tew, Daniel F. Schmidt, Mario Boley
https://arxiv.org/abs/2506.18092 https://

GRASP: Grouped Regression with Adaptive Shrinkage Priors
We introduce GRASP, a simple Bayesian framework for regression with grouped predictors, built on the normal beta prime (NBP) prior. The NBP prior is an adaptive generalization of the horseshoe prior with tunable hyperparameters that control tail behavior, enabling a flexible range of sparsity, from strong shrinkage to ridge-like regularization. Unlike prior work that introduced the group inverse-gamma gamma (GIGG) prior by decomposing the NBP prior into structured hierarchies, we show that dire…

Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments
Woonsang Kang, Joohyung Lee, Seungjun Kim, Jungchan Cho, Yoonseon Oh
https://arxiv.org/abs/2507.05861

Communication-Efficient Module-Wise Federated Learning for Grasp Pose Detection in Cluttered Environments
Grasp pose detection (GPD) is a fundamental capability for robotic autonomy, but its reliance on large, diverse datasets creates significant data privacy and centralization challenges. Federated Learning (FL) offers a privacy-preserving solution, but its application to GPD is hindered by the substantial communication overhead of large models, a key issue for resource-constrained robots. To address this, we propose a novel module-wise FL framework that begins by analyzing the learning dynamics o…

Evening. Can anyone explain why National Rail and Trainline differ so much in the trains they show you? I grasp that TL wants to sell you tickets and NR is an impartial central info hub but I would still expect them to show the same trains, but these screenshots demonstrate that they don't 🤔 NR on left with three trains, TL on right with two. The 7.31 that TL shows doesn't exist on NR's downloadable timetable.

"To grasp the full significance of life is the actor’s duty, to interpret it is his problem, and to express it his dedication."
-Marlon Brando
#acting #coaching #inspiration …

How to tell a vibe coder of lying when they say they check their code.
People who will admit to using LLMs to write code will usually claim that they "carefully check" the output since we all know that LLM code has a lot of errors in it. This is insufficient to address several problems that LLMs cause, including labor issues, digital commons stress/pollution, license violation, and environmental issues, but at least it's they are checking their code carefully we shouldn't assume that it's any worse quality-wise than human-authored code, right?
Well, from principles alone we can expect it to be worse, since checking code the AI wrote is a much more boring task than writing code yourself, so anyone who has ever studied human-computer interaction even a little bit can predict people will quickly slack off, stating to trust the AI way too much, because it's less work. I'm a different domain, the journalist who published an entire "summer reading list" full of nonexistent titles is a great example of this. I'm sure he also intended to carefully check the AI output, but then got lazy. Clearly he did not have a good grasp of the likely failure modes of the tool he was using.
But for vibe coders, there's one easy tell we can look for, at least in some cases: coding in Python without type hints. To be clear, this doesn't apply to novice coders, who might not be aware that type hints are an option. But any serious Python software engineer, whether they used type hints before or not, would know that they're an option. And if you know they're an option, you also know they're an excellent tool for catching code defects, with a very low effort:reward ratio, especially if we assume an LLM generates them. Of the cases where adding types requires any thought at all, 95% of them offer chances to improve your code design and make it more robust. Knowing about but not using type hints in Python is a great sign that you don't care very much about code quality. That's totally fine in many cases: I've got a few demos or jam games in Python with no type hints, and it's okay that they're buggy. I was never going to debug them to a polished level anyways. But if we're talking about a vibe coder who claims that they're taking extra care to check for the (frequent) LLM-induced errors, that's not the situation.
Note that this shouldn't be read as an endorsement of vibe coding for demos or other rough-is-acceptable code: the other ethical issues I skipped past at the start still make it unethical to use in all but a few cases (for example, I have my students use it for a single assignment so they can see for themselves how it's not all it's cracked up to be, and even then they have an option to observe a pre-recorded prompt session instead).

paint() your data when you print them to make them easier to grasp: #rstats #console

GitHub - MilesMcBain/paint: paint the data
paint the data. Contribute to MilesMcBain/paint development by creating an account on GitHub.

I once again confused a US Customs bridge troll by knowing the declaration form off from memory and clearly having a better grasp on USCBP ag biosecurity requirements than he did. I detest bureaucracy, but as a necessity of my former career, I'm very goddamn fucking good at it.

Donald Trump, I believe, wants to pardon Ghislaine Maxwell in exchange for her silence.
Note I said wants to. He might not.
A pardon would rip his base in two.
He may grasp that and not do it. -- But there can be little question that he’s thinking about it.
In fact, on the White House lawn Friday morning, he was asked about a possible Maxwell pardon, and he said:
“I’m allowed to do it.”

Trump Is Teeing Up a Pardon of Epstein Accomplice Ghislaine Maxwell
Think he wouldn’t do it? Really? Did you also think he wouldn’t pardon the January 6 insurrectionists?

Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers
An-Lun Liu, Yu-Wei Chao, Yi-Ting Chen
https://arxiv.org/abs/2507.11287 https://…

Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers
In this paper, we study task-oriented human grasp synthesis, a new grasp synthesis task that demands both task and context awareness. At the core of our method is the task-aware contact maps. Unlike traditional contact maps that only reason about the manipulated object and its relation with the hand, our enhanced maps take into account scene and task information. This comprehensive map is critical for hand-object interaction, enabling accurate grasping poses that align with the task. We propose…

On $\tau$ Spin Use with KKMCee
J. M. John, Ananya Tapadar, Zbigniew Was
https://arxiv.org/abs/2509.04400 https://arxiv.org/pdf/2509.04400

On $τ$ Spin Use with KKMCee
Spin of $τ$ represents an interesting phenomenology data point in both its production from $e^+ e^-$ collision and its subsequent decay. In precision applications such as Belle-II or FCC, where precision tagging is comparable or better than $0.1 \%$ Monte Carlo applications are usually necessary. \texttt{KKMCee}, the precision Monte Carlo program, is assuring such high precision in a broad energy range. But so far, spin effects were not easy to grasp. We introduce into \texttt{KKMCee}, \texttt…

First Plan Then Evaluate: Use a Vectorized Motion Planner for Grasping
Martin Matak, Mohanraj Devendran Ashanti, Karl Van Wyk, Tucker Hermans
https://arxiv.org/abs/2509.07162 ht…

First Plan Then Evaluate: Use a Vectorized Motion Planner for Grasping
Autonomous multi-finger grasping is a fundamental capability in robotic manipulation. Optimization-based approaches show strong performance, but tend to be sensitive to initialization and are potentially time-consuming. As an alternative, the generator-evaluator-planner framework has been proposed. A generator generates grasp candidates, an evaluator ranks the proposed grasps, and a motion planner plans a trajectory to the highest-ranked grasp. If the planner doesn't find a trajectory, a new tr…

Biggest Jump From College to the NFL For Raiders' Rookie https://www.si.com/nfl/raiders/las-vegas-charles-grant-pete-carroll-maxx-crosby-training-camp

Identifying Fine-grained Forms of Populism in Political Discourse: A Case Study on Donald Trump's Presidential Campaigns
Ilias Chalkidis, Stephanie Brandl, Paris Aslanidis
https://arxiv.org/abs/2507.19303

Identifying Fine-grained Forms of Populism in Political Discourse: A Case Study on Donald Trump's Presidential Campaigns
Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of instruction-following tasks, yet their grasp of nuanced social science concepts remains underexplored. This paper examines whether LLMs can identify and classify fine-grained forms of populism, a complex and contested concept in both academic and media debates. To this end, we curate and release novel datasets specifically designed to capture populist discourse. We evaluate a range of pre-trained (larg…

A bibliometric analysis on the current situation and hot trends of the impact of microplastics on soil based on CiteSpace
Yiran Zheng, Yue Quan, Su Yan, Xinting Lv, Yuguanmin Cao, Minjie Fu, Mingji Jin
https://arxiv.org/abs/2507.01520

A bibliometric analysis on the current situation and hot trends of the impact of microplastics on soil based on CiteSpace
This paper aims to comprehensively grasp the research status and development trends of soil microplastics (MPs). It collects studies from the Web of Science Core Collection covering the period from 2013 to 2024. Employing CiteSpace and VOSviewer, the paper conducts in - depth analyses of literature regarding the environmental impacts of microplastics. These analyses involve keyword co - occurrence, clustering, burst term identification, as well as co - occurrence analysis of authors and institu…

Classifying Emergence in Robot Swarms: An Observer-Dependent Approach
Ricardo Vega, Cameron Nowzari
https://arxiv.org/abs/2507.07315 https://

Classifying Emergence in Robot Swarms: An Observer-Dependent Approach
Emergence and swarms are widely discussed topics, yet no consensus exists on their formal definitions. This lack of agreement makes it difficult not only for new researchers to grasp these concepts, but also for experts who may use the same terms to mean different things. Many attempts have been made to objectively define 'swarm' or 'emergence,' with recent work highlighting the role of the external observer. Still, several researchers argue that once an observer's vantage point (e.g., scope, r…

GraSP: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data for SFT and DPO
Bidyapati Pradhan, Surajit Dasgupta, Amit Kumar Saha, Omkar Anustoop, Sriram Puttagunta, Vipul Mittal, Gopal Sarda
https://arxiv.org/abs/2508.15432

GraSP: A Unified Graph-Based Framework for Scalable Generation, Quality Tagging, and Management of Synthetic Data for SFT and DPO
The advancement of large language models (LLMs) is critically dependent on the availability of high-quality datasets for Supervised Fine-Tuning (SFT), alignment tasks like Direct Preference Optimization (DPO), etc. In this work, we present a comprehensive synthetic data generation framework that facilitates scalable, configurable, and high-fidelity generation of synthetic data tailored for these training paradigms. Our approach employs a modular and configuration-based pipeline capable of model…

The judicial cases in OKC are closely linked to the silence and accountability of Tibetan Buddhist Heads of Nyigmapa Lineages in 🇫🇷🇧🇪🇵🇹. To grasp the full extent of responsibility and complacency, let's delve deeper into the matter. #TibetanBuddhism #Responsibility
1/2

Should we teach vibe coding? Here's why not.
Should AI coding be taught in undergrad CS education?
1/2
I teach undergraduate computer science labs, including for intro and more-advanced core courses. I don't publish (non-negligible) scholarly work in the area, but I've got years of craft expertise in course design, and I do follow the academic literature to some degree. In other words, In not the world's leading expert, but I have spent a lot of time thinking about course design, and consider myself competent at it, with plenty of direct experience in what knowledge & skills I can expect from students as they move through the curriculum.
I'm also strongly against most uses of what's called "AI" these days (specifically, generative deep neutral networks as supplied by our current cadre of techbro). There are a surprising number of completely orthogonal reasons to oppose the use of these systems, and a very limited number of reasonable exceptions (overcoming accessibility barriers is an example). On the grounds of environmental and digital-commons-pollution costs alone, using specifically the largest/newest models is unethical in most cases.
But as any good teacher should, I constantly question these evaluations, because I worry about the impact on my students should I eschew teaching relevant tech for bad reasons (and even for his reasons). I also want to make my reasoning clear to students, who should absolutely question me on this. That inspired me to ask a simple question: ignoring for one moment the ethical objections (which we shouldn't, of course; they're very stark), at what level in the CS major could I expect to teach a course about programming with AI assistance, and expect students to succeed at a more technically demanding final project than a course at the same level where students were banned from using AI? In other words, at what level would I expect students to actually benefit from AI coding "assistance?"
To be clear, I'm assuming that students aren't using AI in other aspects of coursework: the topic of using AI to "help you study" is a separate one (TL;DR it's gross value is not negative, but it's mostly not worth the harm to your metacognitive abilities, which AI-induced changes to the digital commons are making more important than ever).
So what's my answer to this question?
If I'm being incredibly optimistic, senior year. Slightly less optimistic, second year of a masters program. Realistic? Maybe never.
The interesting bit for you-the-reader is: why is this my answer? (Especially given that students would probably self-report significant gains at lower levels.) To start with, [this paper where experienced developers thought that AI assistance sped up their work on real tasks when in fact it slowed it down] (https://arxiv.org/abs/2507.09089) is informative. There are a lot of differences in task between experienced devs solving real bugs and students working on a class project, but it's important to understand that we shouldn't have a baseline expectation that AI coding "assistants" will speed things up in the best of circumstances, and we shouldn't trust self-reports of productivity (or the AI hype machine in general).
Now we might imagine that coding assistants will be better at helping with a student project than at helping with fixing bugs in open-source software, since it's a much easier task. For many programming assignments that have a fixed answer, we know that many AI assistants can just spit out a solution based on prompting them with the problem description (there's another elephant in the room here to do with learning outcomes regardless of project success, but we'll ignore this over too, my focus here is on project complexity reach, not learning outcomes). My question is about more open-ended projects, not assignments with an expected answer. Here's a second study (by one of my colleagues) about novices using AI assistance for programming tasks. It showcases how difficult it is to use AI tools well, and some of these stumbling blocks that novices in particular face.
But what about intermediate students? Might there be some level where the AI is helpful because the task is still relatively simple and the students are good enough to handle it? The problem with this is that as task complexity increases, so does the likelihood of the AI generating (or copying) code that uses more complex constructs which a student doesn't understand. Let's say I have second year students writing interactive websites with JavaScript. Without a lot of care that those students don't know how to deploy, the AI is likely to suggest code that depends on several different frameworks, from React to JQuery, without actually setting up or including those frameworks, and of course three students would be way out of their depth trying to do that. This is a general problem: each programming class carefully limits the specific code frameworks and constructs it expects students to know based on the material it covers. There is no feasible way to limit an AI assistant to a fixed set of constructs or frameworks, using current designs. There are alternate designs where this would be possible (like AI search through adaptation from a controlled library of snippets) but those would be entirely different tools.
So what happens on a sizeable class project where the AI has dropped in buggy code, especially if it uses code constructs the students don't understand? Best case, they understand that they don't understand and re-prompt, or ask for help from an instructor or TA quickly who helps them get rid of the stuff they don't understand and re-prompt or manually add stuff they do. Average case: they waste several hours and/or sweep the bugs partly under the rug, resulting in a project with significant defects. Students in their second and even third years of a CS major still have a lot to learn about debugging, and usually have significant gaps in their knowledge of even their most comfortable programming language. I do think regardless of AI we as teachers need to get better at teaching debugging skills, but the knowledge gaps are inevitable because there's just too much to know. In Python, for example, the LLM is going to spit out yields, async functions, try/finally, maybe even something like a while/else, or with recent training data, the walrus operator. I can't expect even a fraction of 3rd year students who have worked with Python since their first year to know about all these things, and based on how students approach projects where they have studied all the relevant constructs but have forgotten some, I'm not optimistic seeing these things will magically become learning opportunities. Student projects are better off working with a limited subset of full programming languages that the students have actually learned, and using AI coding assistants as currently designed makes this impossible. Beyond that, even when the "assistant" just introduces bugs using syntax the students understand, even through their 4th year many students struggle to understand the operation of moderately complex code they've written themselves, let alone written by someone else. Having access to an AI that will confidently offer incorrect explanations for bugs will make this worse.
To be sure a small minority of students will be able to overcome these problems, but that minority is the group that has a good grasp of the fundamentals and has broadened their knowledge through self-study, which earlier AI-reliant classes would make less likely to happen. In any case, I care about the average student, since we already have plenty of stuff about our institutions that makes life easier for a favored few while being worse for the average student (note that our construction of that favored few as the "good" students is a large part of this problem).
To summarize: because AI assistants introduce excess code complexity and difficult-to-debug bugs, they'll slow down rather than speed up project progress for the average student on moderately complex projects. On a fixed deadline, they'll result in worse projects, or necessitate less ambitious project scoping to ensure adequate completion, and I expect this remains broadly true through 4-6 years of study in most programs (don't take this as an endorsement of AI "assistants" for masters students; we've ignored a lot of other problems along the way).
There's a related problem: solving open-ended project assignments well ultimately depends on deeply understanding the problem, and AI "assistants" allow students to put a lot of code in their file without spending much time thinking about the problem or building an understanding of it. This is awful for learning outcomes, but also bad for project success. Getting students to see the value of thinking deeply about a problem is a thorny pedagogical puzzle at the best of times, and allowing the use of AI "assistants" makes the problem much much worse. This is another area I hope to see (or even drive) pedagogical improvement in, for what it's worth.
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GRASPing Anatomy to Improve Pathology Segmentation
Keyi Li, Alexander Jaus, Jens Kleesiek, Rainer Stiefelhagen
https://arxiv.org/abs/2508.03374 https://arx…

GRASPing Anatomy to Improve Pathology Segmentation
Radiologists rely on anatomical understanding to accurately delineate pathologies, yet most current deep learning approaches use pure pattern recognition and ignore the anatomical context in which pathologies develop. To narrow this gap, we introduce GRASP (Guided Representation Alignment for the Segmentation of Pathologies), a modular plug-and-play framework that enhances pathology segmentation models by leveraging existing anatomy segmentation models through pseudolabel integration and featur…

An Implementation of a Visual Stepper in the GRASP Programming System
Panicz Maciej Godek
https://arxiv.org/abs/2508.04859 https://arxiv.org/pdf/2508.04859…

Affordance-Guided Dual-Armed Disassembly Teleoperation for Mating Parts
Gen Sako, Takuya Kiyokawa, Kensuke Harada, Tomoki Ishikura, Naoya Miyaji, Genichiro Matsuda
https://arxiv.org/abs/2508.05937

Affordance-Guided Dual-Armed Disassembly Teleoperation for Mating Parts
Robotic non-destructive disassembly of mating parts remains challenging due to the need for flexible manipulation and the limited visibility of internal structures. This study presents an affordance-guided teleoperation system that enables intuitive human demonstrations for dual-arm fix-and-disassemble tasks for mating parts. The system visualizes feasible grasp poses and disassembly directions in a virtual environment, both derived from the object's geometry, to address occlusions and structur…

"The paper is only interested in the use of the idea, not the idea itself. Hardly anyone can understand the importance of an idea, it is so remarkable. Except that, possibly, some children catch on. And when a child catches on to an idea like that, we have a scientist." ("The value of science, Richard P. Feynman)
Short, but it took me all day to read it because I had to stop and think all the time, and it will take a lot longer to actually grasp it.

So last night, after watching the Russian Victory Day ‘24 video for contrast, I thought “what the hell” & went ahead & watched1️⃣ the PBS YT video of the “June 24 Military2️⃣ Parade” to see if it was as bad as all that
(Short Answer: Yes and No)
But this guy was the one that caught my sympathy as “you poor dude”.

Project-Based Learning in Introductory Quantum Computing Courses: A Case Study on Quantum Algorithms for Medical Imaging
Nischal Binod Gautam, Keith Evan Schubert, Enrique P. Blair
https://arxiv.org/abs/2508.21321

Project-Based Learning in Introductory Quantum Computing Courses: A Case Study on Quantum Algorithms for Medical Imaging
Quantum computing introduces abstract concepts and non-intuitive behaviors that can be challenging for students to grasp through traditional lecture-based instruction alone. This paper demonstrates how Project-Based Learning (PBL) can be leveraged to bridge that gap. This can be done by engaging students in a real-world, interdisciplinary task that combines quantum computing with their field of interest. As part of a similar assignment, we investigated the application of the Harrow-Hassidim-Llo…

Strengthening Programming Comprehension in Large Language Models through Code Generation
Xiaoning Ren, Qiang Hu, Wei Ma, Yan Li, Yao Zhang, Lingxiao Jiang, Yinxing Xue
https://arxiv.org/abs/2508.12620 …

Strengthening Programming Comprehension in Large Language Models through Code Generation
Large language models (LLMs) have recently shown impressive results on diverse code-related tasks, benefiting from large-scale training and instruction tuning. However, studies reveal that their grasp of fundamental programming concepts, such as data flow and control flow, remains shallow, leading to fragile performance when code requires deeper reasoning. This limitation restricts the practical adoption of LLMs in real-world software development. To address this issue, this work introduces a c…

SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes
Yunpeng Mei, Hongjie Cao, Yinqiu Xia, Wei Xiao, Zhaohan Feng, Gang Wang, Jie Chen
https://arxiv.org/abs/2508.20547

SPGrasp: Spatiotemporal Prompt-driven Grasp Synthesis in Dynamic Scenes
Real-time interactive grasp synthesis for dynamic objects remains challenging as existing methods fail to achieve low-latency inference while maintaining promptability. To bridge this gap, we propose SPGrasp (spatiotemporal prompt-driven dynamic grasp synthesis), a novel framework extending segment anything model v2 (SAMv2) for video stream grasp estimation. Our core innovation integrates user prompts with spatiotemporal context, enabling real-time interaction with end-to-end latency as low as …

Crosslisted article(s) found for cs.MM. https://arxiv.org/list/cs.MM/new
[1/1]:
- Predicting User Grasp Intentions in Virtual Reality
Linghao Zeng
https://…

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\'Etale algebras and the Kummer theory of finite Galois modules
Evan M. O'Dorney
https://arxiv.org/abs/2506.11310 https://arx…

Étale algebras and the Kummer theory of finite Galois modules
Galois cohomology groups $H^i(K,M)$ are widely used in algebraic number theory, in such contexts as Selmer groups of elliptic curves, Brauer groups of fields, class field theory, and Iwasawa theory. The standard construction of these groups involves maps out of the absolute Galois group $G_K$, which in many cases of interest (e.g. $K = \mathbb{Q}$) is too large for computation or even for gaining an intuitive grasp. However, for finite $M$, an element of $H^i(K,M)$ can be described by a finite …

Predicting User Grasp Intentions in Virtual Reality
Linghao Zeng
https://arxiv.org/abs/2508.16582 https://arxiv.org/pdf/2508.16582

Predicting User Grasp Intentions in Virtual Reality
Predicting user intentions in virtual reality (VR) is crucial for creating immersive experiences, particularly in tasks involving complex grasping motions where accurate haptic feedback is essential. In this work, we leverage time-series data from hand movements to evaluate both classification and regression approaches across 810 trials with varied object types, sizes, and manipulations. Our findings reveal that classification models struggle to generalize across users, leading to inconsistent …

Manip4Care: Robotic Manipulation of Human Limbs for Solving Assistive Tasks
Yubin Koh, Ahmed H. Qureshi
https://arxiv.org/abs/2508.02649 https://arxiv.org/…

Manip4Care: Robotic Manipulation of Human Limbs for Solving Assistive Tasks
Enabling robots to grasp and reposition human limbs can significantly enhance their ability to provide assistive care to individuals with severe mobility impairments, particularly in tasks such as robot-assisted bed bathing and dressing. However, existing assistive robotics solutions often assume that the human remains static or quasi-static, limiting their effectiveness. To address this issue, we present Manip4Care, a modular simulation pipeline that enables robotic manipulators to grasp and r…

FineGrasp: Towards Robust Grasping for Delicate Objects
Yun Du, Mengao Zhao, Tianwei Lin, Yiwei Jin, Chaodong Huang, Zhizhong Su
https://arxiv.org/abs/2507.05978

FineGrasp: Towards Robust Grasping for Delicate Objects
Recent advancements in robotic grasping have led to its integration as a core module in many manipulation systems. For instance, language-driven semantic segmentation enables the grasping of any designated object or object part. However, existing methods often struggle to generate feasible grasp poses for small objects or delicate components, potentially causing the entire pipeline to fail. To address this issue, we propose a novel grasping method, FineGrasp, which introduces improvements in th…

Querying Large Automotive Software Models: Agentic vs. Direct LLM Approaches
Lukasz Mazur, Nenad Petrovic, James Pontes Miranda, Ansgar Radermacher, Robert Rasche, Alois Knoll
https://arxiv.org/abs/2506.13171

Querying Large Automotive Software Models: Agentic vs. Direct LLM Approaches
Large language models (LLMs) offer new opportunities for interacting with complex software artifacts, such as software models, through natural language. They present especially promising benefits for large software models that are difficult to grasp in their entirety, making traditional interaction and analysis approaches challenging. This paper investigates two approaches for leveraging LLMs to answer questions over software models: direct prompting, where the whole software model is provided …

Interpretable Decision-Making for End-to-End Autonomous Driving
Mona Mirzaie, Bodo Rosenhahn
https://arxiv.org/abs/2508.18898 https://arxiv.org/pdf/2508.18…

Interpretable Decision-Making for End-to-End Autonomous Driving
Trustworthy AI is mandatory for the broad deployment of autonomous vehicles. Although end-to-end approaches derive control commands directly from raw data, interpreting these decisions remains challenging, especially in complex urban scenarios. This is mainly attributed to very deep neural networks with non-linear decision boundaries, making it challenging to grasp the logic behind AI-driven decisions. This paper presents a method to enhance interpretability while optimizing control commands in…

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning
Bingran Chen, Baorun Li, Jian Yang, Yong Liu, Guangyao Zhai
https://arxiv.org/abs/2507.14820

KGN-Pro: Keypoint-Based Grasp Prediction through Probabilistic 2D-3D Correspondence Learning
High-level robotic manipulation tasks demand flexible 6-DoF grasp estimation to serve as a basic function. Previous approaches either directly generate grasps from point-cloud data, suffering from challenges with small objects and sensor noise, or infer 3D information from RGB images, which introduces expensive annotation requirements and discretization issues. Recent methods mitigate some challenges by retaining a 2D representation to estimate grasp keypoints and applying Perspective-n-Point (…

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GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System
Quang Nguyen, Tri Le, Huy Nguyen, Thieu Vo, Tung D. Ta, Baoru Huang, Minh N. Vu, Anh Nguyen
https://arxiv.org/abs/2506.18448

GraspMAS: Zero-Shot Language-driven Grasp Detection with Multi-Agent System
Language-driven grasp detection has the potential to revolutionize human-robot interaction by allowing robots to understand and execute grasping tasks based on natural language commands. However, existing approaches face two key challenges. First, they often struggle to interpret complex text instructions or operate ineffectively in densely cluttered environments. Second, most methods require a training or finetuning step to adapt to new domains, limiting their generation in real-world applicat…

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

Assessing students understanding of the concept of electric potential difference based on the SOLO taxonomy in upper-secondary students for a targeted assessment
Rahmani Fateme, Ahmadi Kalateh Fatemeh
https://arxiv.org/abs/2507.13723

Assessing students understanding of the concept of electric potential difference based on the SOLO taxonomy in upper-secondary students for a targeted assessment
The concept of potential difference is fundamental to understanding electricity in upper secondary physics courses. However, students often find it challenging to grasp. To address this, we designed and implemented a test aimed at assessing students comprehension of potential difference. This researcher developed test, with a reliability coefficient of 0.74, was administered to 92 students in grades 11 and 12. To evaluate the test and gauge the depth of student learning, we employed the Structu…

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Investigating Sensors and Methods in Grasp State Classification in Agricultural Manipulation
Benjamin Walt, Jordan Westphal, Girish Krishnan
https://arxiv.org/abs/2508.11588 htt…

Investigating Sensors and Methods in Grasp State Classification in Agricultural Manipulation
Effective and efficient agricultural manipulation and harvesting depend on accurately understanding the current state of the grasp. The agricultural environment presents unique challenges due to its complexity, clutter, and occlusion. Additionally, fruit is physically attached to the plant, requiring precise separation during harvesting. Selecting appropriate sensors and modeling techniques is critical for obtaining reliable feedback and correctly identifying grasp states. This work investigate…

KnotDLO: Toward Interpretable Knot Tying
Holly Dinkel, Raghavendra Navaratna, Jingyi Xiang, Brian Coltin, Trey Smith, Timothy Bretl
https://arxiv.org/abs/2506.22176

KnotDLO: Toward Interpretable Knot Tying
This work presents KnotDLO, a method for one-handed Deformable Linear Object (DLO) knot tying that is robust to occlusion, repeatable for varying rope initial configurations, interpretable for generating motion policies, and requires no human demonstrations or training. Grasp and target waypoints for future DLO states are planned from the current DLO shape. Grasp poses are computed from indexing the tracked piecewise linear curve representing the DLO state based on the current curve shape and a…

Leveraging Extrinsic Dexterity for Occluded Grasping on Grasp Constraining Walls
Keita Kobashi, Masayoshi Tomizuka
https://arxiv.org/abs/2507.14721 https:/…

Leveraging Extrinsic Dexterity for Occluded Grasping on Grasp Constraining Walls
This study addresses the problem of occluded grasping, where primary grasp configurations of an object are not available due to occlusion with environment. Simple parallel grippers often struggle with such tasks due to limited dexterity and actuation constraints. Prior works have explored object pose reorientation such as pivoting by utilizing extrinsic contacts between an object and an environment feature like a wall, to make the object graspable. However, such works often assume the presence …

CDP: Towards Robust Autoregressive Visuomotor Policy Learning via Causal Diffusion
Jiahua Ma, Yiran Qin, Yixiong Li, Xuanqi Liao, Yulan Guo, Ruimao Zhang
https://arxiv.org/abs/2506.14769

CDP: Towards Robust Autoregressive Visuomotor Policy Learning via Causal Diffusion
Diffusion Policy (DP) enables robots to learn complex behaviors by imitating expert demonstrations through action diffusion. However, in practical applications, hardware limitations often degrade data quality, while real-time constraints restrict model inference to instantaneous state and scene observations. These limitations seriously reduce the efficacy of learning from expert demonstrations, resulting in failures in object localization, grasp planning, and long-horizon task execution. To add…

Effects of variation in system responsiveness on user performance in virtual environments
Benjamin Watson, Neff Walker, William Ribarsky, Victoria Spaulding
https://arxiv.org/abs/2507.18085

Effects of variation in system responsiveness on user performance in virtual environments
System responsiveness (SR) is defined as the elapsed time until a system responds to user control. SR fluctuates over time, so it must be described statistically with mean (MSR) and standard deviation (SDSR). In this paper, we examine SR in virtual environments (VEs), outlining its components and methods of experimental measurement and manipulation. Three studies of MSR and SDSR effects on performance of grasp and placement tasks are then presented. The studies used within-subjects designs with…

GraspQP: Differentiable Optimization of Force Closure for Diverse and Robust Dexterous Grasping
Ren\'e Zurbr\"ugg, Andrei Cramariuc, Marco Hutter
https://arxiv.org/abs/2508.15002

GraspQP: Differentiable Optimization of Force Closure for Diverse and Robust Dexterous Grasping
Dexterous robotic hands enable versatile interactions due to the flexibility and adaptability of multi-fingered designs, allowing for a wide range of task-specific grasp configurations in diverse environments. However, to fully exploit the capabilities of dexterous hands, access to diverse and high-quality grasp data is essential -- whether for developing grasp prediction models from point clouds, training manipulation policies, or supporting high-level task planning with broader action options…

OVGrasp: Open-Vocabulary Grasping Assistance via Multimodal Intent Detection
Chen Hu, Shan Luo, Letizia Gionfrida
https://arxiv.org/abs/2509.04324 https://…

OVGrasp: Open-Vocabulary Grasping Assistance via Multimodal Intent Detection
Grasping assistance is essential for restoring autonomy in individuals with motor impairments, particularly in unstructured environments where object categories and user intentions are diverse and unpredictable. We present OVGrasp, a hierarchical control framework for soft exoskeleton-based grasp assistance that integrates RGB-D vision, open-vocabulary prompts, and voice commands to enable robust multimodal interaction. To enhance generalization in open environments, OVGrasp incorporates a visi…

Reactive In-Air Clothing Manipulation with Confidence-Aware Dense Correspondence and Visuotactile Affordance
Neha Sunil, Megha Tippur, Arnau Saumell, Edward Adelson, Alberto Rodriguez
https://arxiv.org/abs/2509.03889

Reactive In-Air Clothing Manipulation with Confidence-Aware Dense Correspondence and Visuotactile Affordance
Manipulating clothing is challenging due to complex configurations, variable material dynamics, and frequent self-occlusion. Prior systems often flatten garments or assume visibility of key features. We present a dual-arm visuotactile framework that combines confidence-aware dense visual correspondence and tactile-supervised grasp affordance to operate directly on crumpled and suspended garments. The correspondence model is trained on a custom, high-fidelity simulated dataset using a distributi…

GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training
Adithyavairavan Murali, Balakumar Sundaralingam, Yu-Wei Chao, Wentao Yuan, Jun Yamada, Mark Carlson, Fabio Ramos, Stan Birchfield, Dieter Fox, Clemens Eppner
https://arxiv.org/abs/2507.13097

GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training
Grasping is a fundamental robot skill, yet despite significant research advancements, learning-based 6-DOF grasping approaches are still not turnkey and struggle to generalize across different embodiments and in-the-wild settings. We build upon the recent success on modeling the object-centric grasp generation process as an iterative diffusion process. Our proposed framework, GraspGen, consists of a DiffusionTransformer architecture that enhances grasp generation, paired with an efficient discr…

Equivariant Volumetric Grasping
Pinhao Song, Yutong Hu, Pengteng Li, Renaud Detry
https://arxiv.org/abs/2507.18847 https://arxiv.org/pdf/2507.18847

Equivariant Volumetric Grasping
We propose a new volumetric grasp model that is equivariant to rotations around the vertical axis, leading to a significant improvement in sample efficiency. Our model employs a tri-plane volumetric feature representation -- i.e., the projection of 3D features onto three canonical planes. We introduce a novel tri-plane feature design in which features on the horizontal plane are equivariant to 90° rotations, while the sum of features from the other two planes remains invariant to the same tran…

A segmented robot grasping perception neural network for edge AI
Casper Br\"ocheler, Thomas Vroom, Derrick Timmermans, Alan van den Akker, Guangzhi Tang, Charalampos S. Kouzinopoulos, Rico M\"ockel
https://arxiv.org/abs/2507.13970

A segmented robot grasping perception neural network for edge AI
Robotic grasping, the ability of robots to reliably secure and manipulate objects of varying shapes, sizes and orientations, is a complex task that requires precise perception and control. Deep neural networks have shown remarkable success in grasp synthesis by learning rich and abstract representations of objects. When deployed at the edge, these models can enable low-latency, low-power inference, making real-time grasping feasible in resource-constrained environments. This work implements Hea…

ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes
Zeyuan Chen, Qiyang Yan, Yuanpei Chen, Tianhao Wu, Jiyao Zhang, Zihan Ding, Jinzhou Li, Yaodong Yang, Hao Dong
https://arxiv.org/abs/2506.14317

ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes
Dexterous grasping in cluttered scenes presents significant challenges due to diverse object geometries, occlusions, and potential collisions. Existing methods primarily focus on single-object grasping or grasp-pose prediction without interaction, which are insufficient for complex, cluttered scenes. Recent vision-language-action models offer a potential solution but require extensive real-world demonstrations, making them costly and difficult to scale. To address these limitations, we revisit …

A Point Cloud Completion Approach for the Grasping of Partially Occluded Objects and Its Applications in Robotic Strawberry Harvesting
Ali Abouzeid, Malak Mansour, Chengsong Hu, Dezhen Song
https://arxiv.org/abs/2506.14066

A Point Cloud Completion Approach for the Grasping of Partially Occluded Objects and Its Applications in Robotic Strawberry Harvesting
In robotic fruit picking applications, managing object occlusion in unstructured settings poses a substantial challenge for designing grasping algorithms. Using strawberry harvesting as a case study, we present an end-to-end framework for effective object detection, segmentation, and grasp planning to tackle this issue caused by partially occluded objects. Our strategy begins with point cloud denoising and segmentation to accurately locate fruits. To compensate for incomplete scans due to occlu…

Versatile and Generalizable Manipulation via Goal-Conditioned Reinforcement Learning with Grounded Object Detection
Huiyi Wang, Fahim Shahriar, Alireza Azimi, Gautham Vasan, Rupam Mahmood, Colin Bellinger
https://arxiv.org/abs/2507.10814

Versatile and Generalizable Manipulation via Goal-Conditioned Reinforcement Learning with Grounded Object Detection
General-purpose robotic manipulation, including reach and grasp, is essential for deployment into households and workspaces involving diverse and evolving tasks. Recent advances propose using large pre-trained models, such as Large Language Models and object detectors, to boost robotic perception in reinforcement learning. These models, trained on large datasets via self-supervised learning, can process text prompts and identify diverse objects in scenes, an invaluable skill in RL where learnin…

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

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