Tootfinder

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…

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…

»A pragmatic guide to modern CSS colours - part one:
For most developers, the only time they touch colour values is when they copy them from a design file and paste them into their editor. We are developers and not designers, after all.«
— by @… on @…

A pragmatic guide to modern CSS colours - part one
Whether you've got a firm grasp on modern CSS colour capabilities, or you're thinking 'I struggle to understand why I should use modern CSS colours at all', then the first part of this article series, by Kevin Powell, is for you.

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…

This is my curated anarchist playlist, featuring ideas, history, and voices that challenge the existing order.
I recommend it to anyone interested in the topic to watch it in full to grasp its depth and understand why it remains so essential today.
https://www.youtube.com/playlist?list=PLTH

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…

a world you can fully understand and hold in your head and hand. being the snowman in his globe rather than on yours. where's the water come from? the rain, these tanks. where's the power generated? those solar panels and batteries. you know the limits and work with them. self-sufficient. a fantasy irl, but one of the most universal magic cores you can imbue a fantasy with. no man is an island, no man isn't offputtingly hard to grasp.

As failures go, attempting to recall the past is like trying to grasp
the meaning of existence. Both make one feel like a baby clutching at
a basketball: one's palms keep sliding off.
-- Joseph Brodsky

VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generation
Haoran Zhang, Shuanghao Bai, Wanqi Zhou, Yuedi Zhang, Qi Zhang, Pengxiang Ding, Cheng Chi, Donglin Wang, Badong Chen
https://arxiv.org/abs/2510.05827

VCoT-Grasp: Grasp Foundation Models with Visual Chain-of-Thought Reasoning for Language-driven Grasp Generation
Robotic grasping is one of the most fundamental tasks in robotic manipulation, and grasp detection/generation has long been the subject of extensive research. Recently, language-driven grasp generation has emerged as a promising direction due to its practical interaction capabilities. However, most existing approaches either lack sufficient reasoning and generalization capabilities or depend on complex modular pipelines. Moreover, current grasp foundation models tend to overemphasize dialog and…

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

"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 …

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

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…

The Software Observatory: aggregating and analysing software metadata for trend computation and FAIR assessment
Eva Mart\'in del Pico, Josep Llu\'is Gelp\'i, Salvador Capella-Guti\'errez
https://arxiv.org/abs/2510.05705

The Software Observatory: aggregating and analysing software metadata for trend computation and FAIR assessment
In the ever-changing realm of research software development, it is crucial for the scientific community to grasp current trends to identify gaps that can potentially hinder scientific progress. The adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) principles can serve as a proxy to understand those trends and provide a mechanism to propose specific actions. The Software Observatory at OpenEBench (https://openebench.bsc.es/observatory) is a novel web portal that consolidate…

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

Adding another post. This one is a bit less polished, but I want to get it out. As things get harder for everyone, I'm seeing a greater tendency to want to grasp onto revolutionary fiction such as #Andor. I think there's value in that, but it has to come with an informed critique.
> We are so thirsty for hope that we will drink it up, even when that hope comes from a fiction and the truth behind the hope is poison. In Andor, we see the worst elements sacrifice themselves for some of the best. The revolution goes through a process of purification, the complicated elements weeding themselves out to make room for the simplified good, as the rebellion unifies. In reality, this tends to be the opposite how things actually work.
> [...]
> [The Urban Guerilla movement of the 60's through the 80's] centered militant revolution. In doing so, they omitted or cut themselves off from the logistic support needed to sustain such revolutionary activity. The trauma of carrying out violence further isolated and radicalized them. Lacking infrastructure for trauma healing, their decay escalated and became unrecoverable. Ultimately, their revolutionary movements both emulated and reinforced the status quo they were trying to resist.
> There emerges a strange historical parallel that is difficult to see from within the dominant paradigm. The competitive politics of electoralism derives from heroic competition, where people (typically men) compete (often violently) for control over a territory or people. Thus the insurrectionary enters into the very same competition as a challenger, not against the system of domination but for control over it. The success of the revolution, then, does not abolish the system of violent domination but changes rather replaces its management.
> Many modern anarchists will be quick to point out the disconnect between ends and means. While authoritarian projects often assert that "the ends justify the means," and Andor implies the same, anti-authoritarian projects assert the ends and the means are not only united but are, in fact, the same.
This is still very much something I'm actively editing, but I'd still love feedback to help me refine it to it's final form. Typo catches and clarifying questions welcome.
#USPol

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

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.

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…

NotebookLM is great if you quickly want to grasp what a russian article is about and want to ask questions about the content.
Like this russian article about Russian recruitment of saboteurs https://www.currenttime.tv/a/systema-oko-gru/33540426.html

"Ваше сообщение отправлено в ГРУ". Как военная разведка совместно с неонацистом "Топазом" создает...
Российская военная разведка развернула новую схему вербовки диверсантов в США и Европе. ГРУ использует телеграм-ботов, ссылки на которые распространяют Z-блогеры, псевдоукраинские каналы и западные радикалы; один из ботов – "Око Саурона" – пытались запустить даже среди а…

Metacurity is pleased to offer our free and premium subscribers a weekly digest of the best long-form (and longish) infosec-related pieces we couldn't properly fit into our daily news crush.
This week's selection covers
--The time is now to end cybersecurity with AI,
--Prosecutors say PowerSchool hacker was skilled,
--People with facial differences struggle with digital identity systems,
--Autistic teens can't grasp ramifications of digital crimes, …

Best infosec-related long reads for the week of 10/11/25
The time is now to end cybersecurity with AI, Prosecutors say PowerSchool hacker was skilled, People with facial differences struggle with digital identity systems, Autistic teens can't grasp ramifications of digital crimes, Online predator groups can kill teens, Crypto ATMs rip off scam victims

istg people who have opinions on AGI should be required by law to at least grasp the basics of information theory

Michael Moritz says Trump's "H-1B caper will backfire" and shows the "fragile grasp" Trump and his acolytes have of what makes the US tech sector so successful (Michael Moritz/Financial Times)
https://www.ft.com/content/9fc8f69e-52dc-4552-bcde-e39eb6b0…

"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 …

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

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.

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…

HyCoVAD: A Hybrid SSL-LLM Model for Complex Video Anomaly Detection
Mohammad Mahdi Hemmatyar, Mahdi Jafari, Mohammad Amin Yousefi, Mohammad Reza Nemati, Mobin Azadani, Hamid Reza Rastad, Amirmohammad Akbari
https://arxiv.org/abs/2509.22544

HyCoVAD: A Hybrid SSL-LLM Model for Complex Video Anomaly Detection
Video anomaly detection (VAD) is crucial for intelligent surveillance, but a significant challenge lies in identifying complex anomalies, which are events defined by intricate relationships and temporal dependencies among multiple entities rather than by isolated actions. While self-supervised learning (SSL) methods effectively model low-level spatiotemporal patterns, they often struggle to grasp the semantic meaning of these interactions. Conversely, large language models (LLMs) offer powerful…

From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation
KaiWen Wei, Kejun He, Xiaomian Kang, Jie Zhang, Yuming Yang, Jiang Zhong, He Bai, Junnan Zhu
https://arxiv.org/abs/2509.23649

From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation
Generative recommendation, which directly generates item identifiers, has emerged as a promising paradigm for recommendation systems. However, its potential is fundamentally constrained by the reliance on purely autoregressive training. This approach focuses solely on predicting the next item while ignoring the rich internal structure of a user's interaction history, thus failing to grasp the underlying intent. To address this limitation, we propose Masked History Learning (MHL), a novel traini…

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…

GrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloads
Farzaneh Zirak, Farhana Choudhury, Renata Borovica-Gajic
https://arxiv.org/abs/2510.11011

GrASP: A Generalizable Address-based Semantic Prefetcher for Scalable Transactional and Analytical Workloads
Data prefetching--loading data into the cache before it is requested--is essential for reducing I/O overhead and improving database performance. While traditional prefetchers focus on sequential patterns, recent learning-based approaches, especially those leveraging data semantics, achieve higher accuracy for complex access patterns. However, these methods often struggle with today's dynamic, ever-growing datasets and require frequent, timely fine-tuning. Privacy constraints may also restrict a…

at public records in #brooklyn, chicago underground duo playing from their new "hyperglyph." amazing unexpected melds between rob mazurek's chaotic beats/loops & chad taylor's wild intricate drums, which i knew from the album but didn't quite grasp 'til i saw it, plus an utterly gorgeous mbira/electronics duet.

Correlation function metrology for warm dense matter: Recent developments and practical guidelines
Maximilian Peter B\"ohme, Willow Martin, Hannah Bellenbaum, Magaret Berrens, Jan Vorberger, Sebastian Schwalbe, Zhandos Moldabekov, Thomas Gawne, Sebastien Hamel, Brianna Aguilar-Solis, Abhiraj Sharma, Frank Graziani, Tilo D\"oppner, Siegfried Glenzer, Tobias Dornheim, David Bishel

Correlation function metrology for warm dense matter: Recent developments and practical guidelines
X-ray Thomson scattering (XRTS) has emerged as a valuable diagnostic for matter under extreme conditions, as it captures the intricate many-body physics of the probed sample. Recent advances, such as the model-free temperature diagnostic of Dornheim et al. [Nat.Commun. 13, 7911 (2022)], have demonstrated how much information can be extracted directly within the imaginary-time formalism. However, since the imaginary-time formalism is a concept often difficult to grasp, we provide here a systemat…

Watched the new Alan Partridge show on BBC, "How Are You?".
Got a chortle or two from every episode, he's still that same embarrassing cringe character as always. Fun to watch him struggle with reality and, in this case, fail to really grasp mental health.
The character has come a long way since The Day Today and Brasseye. They never would have guessed it back then.
#watching #tv #alanpartridge #bbc

One Agent to Serve All: a Lite-Adaptive Stylized AI Assistant for Millions of Multi-Style Official Accounts
Xingyu Fan, Feifei Li, Wenhui Que, Hailong Li
https://arxiv.org/abs/2509.17788

One Agent to Serve All: a Lite-Adaptive Stylized AI Assistant for Millions of Multi-Style Official Accounts
Conversational agents deployed in industrial-scale official account platforms must generate responses that are both contextually grounded and stylistically aligned-requirements that existing methods struggle to meet. Chain-of-thought (CoT) prompting induces significant latency due to multi-turn reasoning; per-account fine-tuning is computationally prohibitive; and long prompt-based methods degrade the model's ability to grasp injected context and style. In this paper, we propose WeStar, a lite-…

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 …

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…

CEDex: Cross-Embodiment Dexterous Grasp Generation at Scale from Human-like Contact Representations
Zhiyuan Wu, Rolandos Alexandros Potamias, Xuyang Zhang, Zhongqun Zhang, Jiankang Deng, Shan Luo
https://arxiv.org/abs/2509.24661

CEDex: Cross-Embodiment Dexterous Grasp Generation at Scale from Human-like Contact Representations
Cross-embodiment dexterous grasp synthesis refers to adaptively generating and optimizing grasps for various robotic hands with different morphologies. This capability is crucial for achieving versatile robotic manipulation in diverse environments and requires substantial amounts of reliable and diverse grasp data for effective model training and robust generalization. However, existing approaches either rely on physics-based optimization that lacks human-like kinematic understanding or require…

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 …

"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 …

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://…

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

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…

Can't-Miss Play: Devin White's strip-sack vs. Cam Ward gives Raiders possession inside the 5 https://www.nfl.com/videos/can-t-miss-play-devin-white-s-strip-sack-vs-cam-ward-gives-raiders-possession-inside-the-5

DAGDiff: Guiding Dual-Arm Grasp Diffusion to Stable and Collision-Free Grasps
Md Faizal Karim, Vignesh Vembar, Keshab Patra, Gaurav Singh, K Madhava Krishna
https://arxiv.org/abs/2509.21145

DAGDiff: Guiding Dual-Arm Grasp Diffusion to Stable and Collision-Free Grasps
Reliable dual-arm grasping is essential for manipulating large and complex objects but remains a challenging problem due to stability, collision, and generalization requirements. Prior methods typically decompose the task into two independent grasp proposals, relying on region priors or heuristics that limit generalization and provide no principled guarantee of stability. We propose DAGDiff, an end-to-end framework that directly denoises to grasp pairs in the SE(3) x SE(3) space. Our key insigh…

Trajectory control of a suspended load with non-stopping flying carriers
Sofia Girardello, Giulia Michieletto, Angelo Cenedese, Antonio Franchi, Chiara Gabellieri
https://arxiv.org/abs/2510.11413

Trajectory control of a suspended load with non-stopping flying carriers
This paper presents the first closed-loop control framework for cooperative payload transportation with non-stopping flying carriers. Building upon grasp-matrix formulations and internal force redundancy, we propose a feedback wrench controller that actively regulates the payload's pose while an optimization layer dynamically shapes internal-force oscillations to guarantee persistent carrier motion. Preliminary experimental results on multirotor UAVs validate the model assumptions, and numerica…

Cross-Embodiment Dexterous Hand Articulation Generation via Morphology-Aware Learning
Heng Zhang, Kevin Yuchen Ma, Mike Zheng Shou, Weisi Lin, Yan Wu
https://arxiv.org/abs/2510.06068

Cross-Embodiment Dexterous Hand Articulation Generation via Morphology-Aware Learning
Dexterous grasping with multi-fingered hands remains challenging due to high-dimensional articulations and the cost of optimization-based pipelines. Existing end-to-end methods require training on large-scale datasets for specific hands, limiting their ability to generalize across different embodiments. We propose an eigengrasp-based, end-to-end framework for cross-embodiment grasp generation. From a hand's morphology description, we derive a morphology embedding and an eigengrasp set. Conditio…

Do Large Language Models Understand Word Senses?
Domenico Meconi, Simone Stirpe, Federico Martelli, Leonardo Lavalle, Roberto Navigli
https://arxiv.org/abs/2509.13905 https://…

Do Large Language Models Understand Word Senses?
Understanding the meaning of words in context is a fundamental capability for Large Language Models (LLMs). Despite extensive evaluation efforts, the extent to which LLMs show evidence that they truly grasp word senses remains underexplored. In this paper, we address this gap by evaluating both i) the Word Sense Disambiguation (WSD) capabilities of instruction-tuned LLMs, comparing their performance to state-of-the-art systems specifically designed for the task, and ii) the ability of two top-p…

Score the Steps, Not Just the Goal: VLM-Based Subgoal Evaluation for Robotic Manipulation
Ramy ElMallah, Krish Chhajer, Chi-Guhn Lee
https://arxiv.org/abs/2509.19524 https://

Score the Steps, Not Just the Goal: VLM-Based Subgoal Evaluation for Robotic Manipulation
Robot learning papers typically report a single binary success rate (SR), which obscures where a policy succeeds or fails along a multi-step manipulation task. We argue that subgoal-level reporting should become routine: for each trajectory, a vector of per-subgoal SRs that makes partial competence visible (e.g., grasp vs. pour). We propose a blueprint for StepEval, a cost-aware plug-in evaluation framework that utilizes vision-language models (VLMs) as automated judges of subgoal outcomes from…

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

Vision-Guided Targeted Grasping and Vibration for Robotic Pollination in Controlled Environments
Jaehwan Jeong, Tuan-Anh Vu, Radha Lahoti, Jiawen Wang, Vivek Alumootil, Sangpil Kim, M. Khalid Jawed
https://arxiv.org/abs/2510.06146

Vision-Guided Targeted Grasping and Vibration for Robotic Pollination in Controlled Environments
Robotic pollination offers a promising alternative to manual labor and bumblebee-assisted methods in controlled agriculture, where wind-driven pollination is absent and regulatory restrictions limit the use of commercial pollinators. In this work, we present and validate a vision-guided robotic framework that uses data from an end-effector mounted RGB-D sensor and combines 3D plant reconstruction, targeted grasp planning, and physics-based vibration modeling to enable precise pollination. First…

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…

High-Bandwidth Tactile-Reactive Control for Grasp Adjustment
Yonghyeon Lee, Tzu-Yuan Lin, Alexander Alexiev, Sangbae Kim
https://arxiv.org/abs/2509.15876 https://

High-Bandwidth Tactile-Reactive Control for Grasp Adjustment
Vision-only grasping systems are fundamentally constrained by calibration errors, sensor noise, and grasp pose prediction inaccuracies, leading to unavoidable contact uncertainty in the final stage of grasping. High-bandwidth tactile feedback, when paired with a well-designed tactile-reactive controller, can significantly improve robustness in the presence of perception errors. This paper contributes to controller design by proposing a purely tactile-feedback grasp-adjustment algorithm. The pro…

Score the Steps, Not Just the Goal: VLM-Based Subgoal Evaluation for Robotic Manipulation
Ramy ElMallah, Krish Chhajer, Chi-Guhn Lee
https://arxiv.org/abs/2509.19524 https://

Score the Steps, Not Just the Goal: VLM-Based Subgoal Evaluation for Robotic Manipulation
Robot learning papers typically report a single binary success rate (SR), which obscures where a policy succeeds or fails along a multi-step manipulation task. We argue that subgoal-level reporting should become routine: for each trajectory, a vector of per-subgoal SRs that makes partial competence visible (e.g., grasp vs. pour). We propose a blueprint for StepEval, a cost-aware plug-in evaluation framework that utilizes vision-language models (VLMs) as automated judges of subgoal outcomes from…

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

Pun Unintended: LLMs and the Illusion of Humor Understanding
Alessandro Zangari, Matteo Marcuzzo, Andrea Albarelli, Mohammad Taher Pilehvar, Jose Camacho-Collados
https://arxiv.org/abs/2509.12158

Pun Unintended: LLMs and the Illusion of Humor Understanding
Puns are a form of humorous wordplay that exploits polysemy and phonetic similarity. While LLMs have shown promise in detecting puns, we show in this paper that their understanding often remains shallow, lacking the nuanced grasp typical of human interpretation. By systematically analyzing and reformulating existing pun benchmarks, we demonstrate how subtle changes in puns are sufficient to mislead LLMs. Our contributions include comprehensive and nuanced pun detection benchmarks, human evaluat…

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…

HOGraspFlow: Exploring Vision-based Generative Grasp Synthesis with Hand-Object Priors and Taxonomy Awareness
Yitian Shi, Zicheng Guo, Rosa Wolf, Edgar Welte, Rania Rayyes
https://arxiv.org/abs/2509.16871

HOGraspFlow: Exploring Vision-based Generative Grasp Synthesis with Hand-Object Priors and Taxonomy Awareness
We propose Hand-Object\emph{(HO)GraspFlow}, an affordance-centric approach that retargets a single RGB with hand-object interaction (HOI) into multi-modal executable parallel jaw grasps without explicit geometric priors on target objects. Building on foundation models for hand reconstruction and vision, we synthesize $SE(3)$ grasp poses with denoising flow matching (FM), conditioned on the following three complementary cues: RGB foundation features as visual semantics, HOI contact reconstructio…

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DHAGrasp: Synthesizing Affordance-Aware Dual-Hand Grasps with Text Instructions
Quanzhou Li, Zhonghua Wu, Jingbo Wang, Chen Change Loy, Bo Dai
https://arxiv.org/abs/2509.22175 h…

DHAGrasp: Synthesizing Affordance-Aware Dual-Hand Grasps with Text Instructions
Learning to generate dual-hand grasps that respect object semantics is essential for robust hand-object interaction but remains largely underexplored due to dataset scarcity. Existing grasp datasets predominantly focus on single-hand interactions and contain only limited semantic part annotations. To address these challenges, we introduce a pipeline, SymOpt, that constructs a large-scale dual-hand grasp dataset by leveraging existing single-hand datasets and exploiting object and hand symmetrie…

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…

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

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…

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…

GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning
Jizhuo Chen, Diwen Liu, Jiaming Wang, Harold Soh
https://arxiv.org/abs/2509.11594 https://

GBPP: Grasp-Aware Base Placement Prediction for Robots via Two-Stage Learning
GBPP is a fast learning based scorer that selects a robot base pose for grasping from a single RGB-D snapshot. The method uses a two stage curriculum: (1) a simple distance-visibility rule auto-labels a large dataset at low cost; and (2) a smaller set of high fidelity simulation trials refines the model to match true grasp outcomes. A PointNet++ style point cloud encoder with an MLP scores dense grids of candidate poses, enabling rapid online selection without full task-and-motion optimization.…

T(R,O) Grasp: Efficient Graph Diffusion of Robot-Object Spatial Transformation for Cross-Embodiment Dexterous Grasping
Xin Fei, Zhixuan Xu, Huaicong Fang, Tianrui Zhang, Lin Shao
https://arxiv.org/abs/2510.12724

T(R,O) Grasp: Efficient Graph Diffusion of Robot-Object Spatial Transformation for Cross-Embodiment Dexterous Grasping
Dexterous grasping remains a central challenge in robotics due to the complexity of its high-dimensional state and action space. We introduce T(R,O) Grasp, a diffusion-based framework that efficiently generates accurate and diverse grasps across multiple robotic hands. At its core is the T(R,O) Graph, a unified representation that models spatial transformations between robotic hands and objects while encoding their geometric properties. A graph diffusion model, coupled with an efficient inverse…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/2]:
- Grasp Pre-shape Selection by Synthetic Training: Eye-in-hand Shared Control on the Hannes Prosthesis
Federico Vasile, Elisa Maiettini, Giulia Pasquale, Astrid Florio, Nicol\`o Boccardo, Lorenzo Natale

Towards Developing Standards and Guidelines for Robot Grasping and Manipulation Pipelines in the COMPARE Ecosystem
Huajing Zhao, Brian Flynn, Adam Norton, Holly Yanco
https://arxiv.org/abs/2509.22801

Towards Developing Standards and Guidelines for Robot Grasping and Manipulation Pipelines in the COMPARE Ecosystem
The COMPARE Ecosystem aims to improve the compatibility and benchmarking of open-source products for robot manipulation through a series of activities. One such activity is the development of standards and guidelines to specify modularization practices at the component-level for individual modules (e.g., perception, grasp planning, motion planning) and integrations of components that form robot manipulation capabilities at the pipeline-level. This paper briefly reviews our work-in-progress to d…

LLM-Handover:Exploiting LLMs for Task-Oriented Robot-Human Handovers
Andreea Tulbure, Rene Zurbruegg, Timm Grigat, Marco Hutter
https://arxiv.org/abs/2509.24706 https://

LLM-Handover:Exploiting LLMs for Task-Oriented Robot-Human Handovers
Effective human-robot collaboration depends on task-oriented handovers, where robots present objects in ways that support the partners intended use. However, many existing approaches neglect the humans post-handover action, relying on assumptions that limit generalizability. To address this gap, we propose LLM-Handover, a novel framework that integrates large language model (LLM)-based reasoning with part segmentation to enable context-aware grasp selection and execution. Given an RGB-D image a…

Learning-Based Collaborative Control for Bi-Manual Tactile-Reactive Grasping
Leonel Giacobbe, Jingdao Chen, Chuangchuang Sun
https://arxiv.org/abs/2509.22421 https://

Learning-Based Collaborative Control for Bi-Manual Tactile-Reactive Grasping
Grasping is a core task in robotics with various applications. However, most current implementations are primarily designed for rigid items, and their performance drops considerably when handling fragile or deformable materials that require real-time feedback. Meanwhile, tactile-reactive grasping focuses on a single agent, which limits their ability to grasp and manipulate large, heavy objects. To overcome this, we propose a learning-based, tactile-reactive multi-agent Model Predictive Controll…

BiGraspFormer: End-to-End Bimanual Grasp Transformer
Kangmin Kim, Seunghyeok Back, Geonhyup Lee, Sangbeom Lee, Sangjun Noh, Kyoobin Lee
https://arxiv.org/abs/2509.19142 https://…

Joint Flow Trajectory Optimization For Feasible Robot Motion Generation from Video Demonstrations
Xiaoxiang Dong, Matthew Johnson-Roberson, Weiming Zhi
https://arxiv.org/abs/2509.20703

Joint Flow Trajectory Optimization For Feasible Robot Motion Generation from Video Demonstrations
Learning from human video demonstrations offers a scalable alternative to teleoperation or kinesthetic teaching, but poses challenges for robot manipulators due to embodiment differences and joint feasibility constraints. We address this problem by proposing the Joint Flow Trajectory Optimization (JFTO) framework for grasp pose generation and object trajectory imitation under the video-based Learning-from-Demonstration (LfD) paradigm. Rather than directly imitating human hand motions, our metho…

AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation
Konstantin Gubernatorov, Artem Voronov, Roman Voronov, Sergei Pasynkov, Stepan Perminov, Ziang Guo, Dzmitry Tsetserukou
https://arxiv.org/abs/2509.21006

AnywhereVLA: Language-Conditioned Exploration and Mobile Manipulation
We address natural language pick-and-place in unseen, unpredictable indoor environments with AnywhereVLA, a modular framework for mobile manipulation. A user text prompt serves as an entry point and is parsed into a structured task graph that conditions classical SLAM with LiDAR and cameras, metric semantic mapping, and a task-aware frontier exploration policy. An approach planner then selects visibility and reachability aware pre grasp base poses. For interaction, a compact SmolVLA manipulatio…

D3Grasp: Diverse and Deformable Dexterous Grasping for General Objects
Keyu Wang, Bingcong Lu, Zhengxue Cheng, Hengdi Zhang, Li Song
https://arxiv.org/abs/2509.19892 https://

D3Grasp: Diverse and Deformable Dexterous Grasping for General Objects
Achieving diverse and stable dexterous grasping for general and deformable objects remains a fundamental challenge in robotics, due to high-dimensional action spaces and uncertainty in perception. In this paper, we present D3Grasp, a multimodal perception-guided reinforcement learning framework designed to enable Diverse and Deformable Dexterous Grasping. We firstly introduce a unified multimodal representation that integrates visual and tactile perception to robustly grasp common objects with …

Beyond Anthropomorphism: Enhancing Grasping and Eliminating a Degree of Freedom by Fusing the Abduction of Digits Four and Five
Simon Fritsch (Soft Robotics Lab, IRIS, D-MAVT, ETH Zurich, Switzerland), Liam Achenbach (Soft Robotics Lab, IRIS, D-MAVT, ETH Zurich, Switzerland), Riccardo Bianco (Soft Robotics Lab, IRIS, D-MAVT, ETH Zurich, Switzerland), Nicola Irmiger (Soft Robotics Lab, IRIS, D-MAVT, ETH Zurich, Switzerland), Gawain Marti (Soft Robotics Lab, IRIS, D-MAVT, ETH Zurich, Swi…

Beyond Anthropomorphism: Enhancing Grasping and Eliminating a Degree of Freedom by Fusing the Abduction of Digits Four and Five
This paper presents the SABD hand, a 16-degree-of-freedom (DoF) robotic hand that departs from purely anthropomorphic designs to achieve an expanded grasp envelope, enable manipulation poses beyond human capability, and reduce the required number of actuators. This is achieved by combining the adduction/abduction (Add/Abd) joint of digits four and five into a single joint with a large range of motion. The combined joint increases the workspace of the digits by 400\% and reduces the required DoF…

Tendon-Based Proprioception in an Anthropomorphic Underactuated Robotic Hand with Series Elastic Actuators
Jae-Hyun Lee, Jonghoo Park, Kyu-Jin Cho
https://arxiv.org/abs/2509.12969

Tendon-Based Proprioception in an Anthropomorphic Underactuated Robotic Hand with Series Elastic Actuators
Anthropomorphic underactuated hands are widely employed for their versatility and structural simplicity. In such systems, compact sensing integration and proper interpretation aligned with underactuation are crucial for realizing practical grasp functionalities. This study proposes an anthropomorphic underactuated hand that achieves comprehensive situational awareness of hand-object interaction, utilizing tendon-based proprioception provided by series elastic actuators (SEAs). We developed a co…

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…

Placeit! A Framework for Learning Robot Object Placement Skills
Amina Ferrad, Johann Huber, Fran\c{c}ois H\'el\'enon, Julien Gleyze, Mahdi Khoramshahi, St\'ephane Doncieux
https://arxiv.org/abs/2510.09267

Placeit! A Framework for Learning Robot Object Placement Skills
Robotics research has made significant strides in learning, yet mastering basic skills like object placement remains a fundamental challenge. A key bottleneck is the acquisition of large-scale, high-quality data, which is often a manual and laborious process. Inspired by Graspit!, a foundational work that used simulation to automatically generate dexterous grasp poses, we introduce Placeit!, an evolutionary-computation framework for generating valid placement positions for rigid objects. Placei…