Tootfinder

Defence ministers today vs a defence ministers a mere nine years ago:

Ok NLziet ... Er zijn toch echt grenzen aan wat ik waardeer.. 😅😅😅
Zelfs nog nooit per ongeluk naar gekeken in al die jaren!

#OTD 2022.

T Minus 20
The year is 2005… Anakin turns to the dark side, YouTube makes its debut...
Great Australian Pods Podcast Directory: #GreatAusPods

Series C, Episode 01 - Aftermath
CALLY: Jenna's gone with Blake.
AVON: So let's get on with it. [Explosion. Avon is unconscious]
CALLY: He's out cold. We must get him into a capsule fast. And then you can put Orac in with him. Come on, quick!
https://blake.torpidity.net/m/301/14

Exploring the valley of the Kern River from the air and on the ground this past weekend
#aviation

»The Job Market Is Hell:
Young people are using ChatGPT to write their applications; HR is using AI to read them; no one is getting hired.«
It is not about yours or a job, it is about feeding the AI ​​with data fully to have it presented as "varied & intelligent". The merciless exploitation of people and their privacy. Anyone who emphasizes that they have nothing to hide has not understood data theft.
😵‍💫

The Job Market Is Hell
Young people are using ChatGPT to write their applications; HR is using AI to read them; no one is getting hired.

Nice overview of the role of #Nurse Scientists employed in #US #hospitals...

wie jede herrschaft von menschen über menschen, braucht auch die #demokratie den dünger aus den Leichen der ihr unterworfenen, damit sie blühen kann.
#deutschland #bundeswehr

Fascist Paramilitary invaders spray a large canister of pepper spray directly into family car, hitting 1-year-old baby. [Cicero, IL]
"They were simply shopping at Sam's Club," said Matt, who filmed this video. "ICE agents drove by and sprayed pepper spray directly inside the car, where their baby and 16-year-old sister sat."
Article:

AgriCoach Wealth & Wisdom Podcast By The Financial Bloke
For Farming Families Who Want More Money matters...
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/the-financial-bloke-wealth-and-wisdom/

Wordle 1,575 3/6
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sub par streak: 1
last 9: birdies: 7, pars: 2
last 18: birdies: 14, pars: 4
#Wordle #WordleResults #WordPuzzle

Quo vadis Naher Osten? Von Moshe A. Friedman.
#Gaza #Israel

Quo vadis Naher Osten? - Europa Blog
98

RE: https://mastodon.social/@jonasdowney/115526595030911835
My favorite weather app could be your favorite weather app too!

I hadn't realised there was Salt, N and Pepa.
#TOTP

I can attest that being at an indoor range near someone with a short barreled AR is more than unpleasant, it’s discombobulating and, as it turns out, brain scrambling. I’ve switched to an outdoor range for that reason.
https://www.n…

How Gun Blasts From Indoor Shooting May Cause Brain Injuries
The Times tested the blast waves of several popular civilian guns at an indoor range and found that repeated firing could add up to potentially harmful exposure.

TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models
Zongzheng Zhang, Haobo Xu, Zhuo Yang, Chenghao Yue, Zehao Lin, Huan-ang Gao, Ziwei Wang, Hao Zhao
https://arxiv.org/abs/2509.07962

TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models
Many robotic manipulation tasks require sensing and responding to force signals such as torque to assess whether the task has been successfully completed and to enable closed-loop control. However, current Vision-Language-Action (VLA) models lack the ability to integrate such subtle physical feedback. In this work, we explore Torque-aware VLA models, aiming to bridge this gap by systematically studying the design space for incorporating torque signals into existing VLA architectures. We identif…

Why the gewaltmonopol want that we pay our Lohn for moving our body through space?

We board planes by zones. Everyone who flies understands this.
Why must we have exceptions? (Families with small children, Active Duty Military, people who fly this airline entirely too much, etc)
Why not sort this out ahead of time and give them the appropriate zone?
Wouldn’t that make everything easier for everyone?

Quo vadis Naher Osten? Von Moshe A. Friedman.
#Gaza #Israel

I haven’t seen it mentioned anywhere, but isn’t the origin of the cool S basically a traced bend open paperclip? That’s the shape you get, dependent on the exact model, right? To me the mystery remains who noticed that at first, but other than that… ?????
https://en.wikipedia.org/wiki/Cool_S

Those trumpets sound remarkably synthesised.
Almost... like... they're... not... plugged in?!
#TOTP #Sonia

Graph-Fused Vision-Language-Action for Policy Reasoning in Multi-Arm Robotic Manipulation
Shunlei Li, Longsen Gao, Jiuwen Cao, Yingbai Hu
https://arxiv.org/abs/2509.07957 https:…

Graph-Fused Vision-Language-Action for Policy Reasoning in Multi-Arm Robotic Manipulation
Acquiring dexterous robotic skills from human video demonstrations remains a significant challenge, largely due to conventional reliance on low-level trajectory replication, which often fails to generalize across varying objects, spatial layouts, and manipulator configurations. To address this limitation, we introduce Graph-Fused Vision-Language-Action (GF-VLA), a unified framework that enables dual-arm robotic systems to perform task-level reasoning and execution directly from RGB-D human demo…

Headed home from Boston, MA. Taking a Lyft to airport. I like that Massachusetts has number plates (tags) on front and rear bumpers. Makes it much easier to identify our ride.

Was der palästinensische Protest vor der Börse in Brüssel für unsere Demokratie bedeutet. Von Bram Van Renterghem.
https://europa.blog/de/was-der-palaestinensische-protest-an-der-boerse-ueber-unsere-demokratie-sagt/

Was der palästinensische Protest vor der Börse in Brüssel für unsere Demokratie bedeutet - Europa Blog
137

Einen schönen Brückentag, an alle die ihn feiern.
#Carolabrücke

RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction
Zheyuan Hu, Robyn Wu, Naveen Enock, Jasmine Li, Riya Kadakia, Zackory Erickson, Aviral Kumar
https://arxiv.org/abs/2509.07953

RaC: Robot Learning for Long-Horizon Tasks by Scaling Recovery and Correction
Modern paradigms for robot imitation train expressive policy architectures on large amounts of human demonstration data. Yet performance on contact-rich, deformable-object, and long-horizon tasks plateau far below perfect execution, even with thousands of expert demonstrations. This is due to the inefficiency of existing ``expert'' data collection procedures based on human teleoperation. To address this issue, we introduce RaC, a new phase of training on human-in-the-loop rollouts after imitati…

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sub par streak: 9
last 9: birdies: 9
last 18: eagles: 1, birdies: 14, pars: 3
#Wordle #WordleResults #WordPuzzle

KPop Demon Hunters are without a doubt great and all but this is my periodic reminder that AC/DC shielded us in 2009 with this S-Tier performance: the pinnacle of Rock’n'Roll!
https://youtu.be/QMvE0yFnR0I

I’m checking in with Better Ballot Georgia to see what I can do to promote Ranked Choice Voting in GA. Care to join me? 9/17 19:00-20:00 via Zoom
https://www.betterballotgeorgia.org/bbga_statewide_20250917?recruiter_id=95824

Programmable Locking Cells (PLC) for Modular Robots with High Stiffness Tunability and Morphological Adaptability
Jianshu Zhou, Wei Chen, Junda Huang, Boyuan Liang, Yunhui Liu, Masayoshi Tomizuka
https://arxiv.org/abs/2509.07916

Programmable Locking Cells (PLC) for Modular Robots with High Stiffness Tunability and Morphological Adaptability
Robotic systems operating in unstructured environments require the ability to switch between compliant and rigid states to perform diverse tasks such as adaptive grasping, high-force manipulation, shape holding, and navigation in constrained spaces, among others. However, many existing variable stiffness solutions rely on complex actuation schemes, continuous input power, or monolithic designs, limiting their modularity and scalability. This paper presents the Programmable Locking Cell (PLC)-a …

I got „Pilgrim In The Microworld“ from the library after seeing this Jacob Geller essay and it’s absolutely brilliant so far!
https://youtu.be/cSrP1ipkes0

💉💉(Covid-19, Flu)
Harder than it should have been.
It wasn’t that hard - virtual request for script from my Doc. Pharmacy asked if I was high risk and I said yes. $0 for the script. $0 for the jabs.
It’s pretty crazy that I need a prescription for the COVID vaccine, but not the flu vaccine.
#ushealthcare

Unlocking Stopped-Rotor Flight: Development and Validation of SPERO, a Novel UAV Platform
Kristan Hilby, Ian Hunter
https://arxiv.org/abs/2509.07812 https://

Unlocking Stopped-Rotor Flight: Development and Validation of SPERO, a Novel UAV Platform
Stop-rotor aircraft have long been proposed as the ideal vertical takeoff and landing (VTOL) aircraft for missions with equal time spent in both flight regimes, such as agricultural monitoring, search and rescue, and last-mile delivery. Featuring a central lifting surface that rotates in VTOL to generate vertical thrust and locks in forward flight to generate passive lift, the stop-rotor offers the potential for high efficiency across both modes. However, practical implementation has remained i…

Wordle 1,546 3/6
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sub par streak: 10
last 9: birdies: 9
last 18: eagles: 1, birdies: 14, pars: 3
#Wordle #WordleResults #WordPuzzle

Fault Tolerant Control of a Quadcopter using Reinforcement Learning
Muzaffar Habib, Adnan Maqsood, Adnan Fayyaz ud Din
https://arxiv.org/abs/2509.07707 https://

Fault Tolerant Control of a Quadcopter using Reinforcement Learning
This study presents a novel reinforcement learning (RL)-based control framework aimed at enhancing the safety and robustness of the quadcopter, with a specific focus on resilience to in-flight one propeller failure. Addressing the critical need of a robust control strategy for maintaining a desired altitude for the quadcopter to safe the hardware and the payload in physical applications. The proposed framework investigates two RL methodologies Dynamic Programming (DP) and Deep Deterministic Pol…

Robust Radar SLAM for Vehicle Parking Applications
Luis Diener, Jens Kalkkuhl, Markus Enzweiler
https://arxiv.org/abs/2509.07683 https://arxiv.org/pdf/2509…

Robust Radar SLAM for Vehicle Parking Applications
We address ego-motion estimation for automated parking, where centimeter-level accuracy is crucial due to tight spaces and nearby obstacles. Traditional methods using inertial-measurement units and wheel encoders require calibration, making them costly and time-consuming. To overcome this, we propose a radar-based simultaneous localization and mapping (SLAM) approach that leverages the robustness of radar to adverse weather and support for online calibration. Our robocentric formulation fuses f…

Temporal Counterfactual Explanations of Behaviour Tree Decisions
Tamlin Love, Antonio Andriella, Guillem Aleny\`a
https://arxiv.org/abs/2509.07674 https://…

Temporal Counterfactual Explanations of Behaviour Tree Decisions
Explainability is a critical tool in helping stakeholders understand robots. In particular, the ability for robots to explain why they have made a particular decision or behaved in a certain way is useful in this regard. Behaviour trees are a popular framework for controlling the decision-making of robots and other software systems, and thus a natural question to ask is whether or not a system driven by a behaviour tree is capable of answering "why" questions. While explainability for behaviour…

Collaborative Exploration with a Marsupial Ground-Aerial Robot Team through Task-Driven Map Compression
Angelos Zacharia, Mihir Dharmadhikari, Kostas Alexis
https://arxiv.org/abs/2509.07655

Collaborative Exploration with a Marsupial Ground-Aerial Robot Team through Task-Driven Map Compression
Efficient exploration of unknown environments is crucial for autonomous robots, especially in confined and large-scale scenarios with limited communication. To address this challenge, we propose a collaborative exploration framework for a marsupial ground-aerial robot team that leverages the complementary capabilities of both platforms. The framework employs a graph-based path planning algorithm to guide exploration and deploy the aerial robot in areas where its expected gain significantly exce…

Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network
Yanlong Peng, Zhigang Wang, Ziwen He, Pengxu Chang, Chuangchuang Zhou, Yu Yan, Ming Chen
https://arxiv.org/abs/2509.07646

Decoding RobKiNet: Insights into Efficient Training of Robotic Kinematics Informed Neural Network
In robots task and motion planning (TAMP), it is crucial to sample within the robot's configuration space to meet task-level global constraints and enhance the efficiency of subsequent motion planning. Due to the complexity of joint configuration sampling under multi-level constraints, traditional methods often lack efficiency. This paper introduces the principle of RobKiNet, a kinematics-informed neural network, for end-to-end sampling within the Continuous Feasible Set (CFS) under multiple co…

Can SSD-Mamba2 Unlock Reinforcement Learning for End-to-End Motion Control?
Gavin Tao, Yinuo Wang, Jinzhao Zhou
https://arxiv.org/abs/2509.07593 https://ar…

Can SSD-Mamba2 Unlock Reinforcement Learning for End-to-End Motion Control?
End-to-end reinforcement learning for motion control promises unified perception-action policies that scale across embodiments and tasks, yet most deployed controllers are either blind (proprioception-only) or rely on fusion backbones with unfavorable compute-memory trade-offs. Recurrent controllers struggle with long-horizon credit assignment, and Transformer-based fusion incurs quadratic cost in token length, limiting temporal and spatial context. We present a vision-driven cross-modal RL fra…

Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding
Bartlomiej Kulecki, Dominik Belter
https://arxiv.org/abs/2509.07542 https://…

Improving Machine Learning-Based Robot Self-Collision Checking with Input Positional Encoding
This manuscript investigates the integration of positional encoding -- a technique widely used in computer graphics -- into the input vector of a binary classification model for self-collision detection. The results demonstrate the benefits of incorporating positional encoding, which enhances classification accuracy by enabling the model to better capture high-frequency variations, leading to a more detailed and precise representation of complex collision patterns. The manuscript shows that mac…

OmniMap: A General Mapping Framework Integrating Optics, Geometry, and Semantics
Yinan Deng, Yufeng Yue, Jianyu Dou, Jingyu Zhao, Jiahui Wang, Yujie Tang, Yi Yang, Mengyin Fu
https://arxiv.org/abs/2509.07500

OmniMap: A General Mapping Framework Integrating Optics, Geometry, and Semantics
Robotic systems demand accurate and comprehensive 3D environment perception, requiring simultaneous capture of photo-realistic appearance (optical), precise layout shape (geometric), and open-vocabulary scene understanding (semantic). Existing methods typically achieve only partial fulfillment of these requirements while exhibiting optical blurring, geometric irregularities, and semantic ambiguities. To address these challenges, we propose OmniMap. Overall, OmniMap represents the first online m…

Flexible Morphing Aerial Robot with Inflatable Structure for Perching-based Human-Robot Interaction
Ayano Miyamichi, Moju Zhao, Kazuki Sugihara, Junichiro Sugihara, Masanori Konishi, Kunio Kojima, Kei Okada, Masayuki Inaba
https://arxiv.org/abs/2509.07496

Flexible Morphing Aerial Robot with Inflatable Structure for Perching-based Human-Robot Interaction
Birds in nature perform perching not only for rest but also for interaction with human such as the relationship with falconers. Recently, researchers achieve perching-capable aerial robots as a way to save energy, and deformable structure demonstrate significant advantages in efficiency of perching and compactness of configuration. However, ensuring flight stability remains challenging for deformable aerial robots due to the difficulty of controlling flexible arms. Furthermore, perching for hum…

Safe and Non-Conservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barriers
Rui Yang, Lei Zheng, Shuzhi Sam Ge, Jun Ma
https://arxiv.org/abs/2509.07464

Safe and Non-Conservative Contingency Planning for Autonomous Vehicles via Online Learning-Based Reachable Set Barriers
Autonomous vehicles must navigate dynamically uncertain environments while balancing the safety and driving efficiency. This challenge is exacerbated by the unpredictable nature of surrounding human-driven vehicles (HVs) and perception inaccuracies, which require planners to adapt to evolving uncertainties while maintaining safe trajectories. Overly conservative planners degrade driving efficiency, while deterministic approaches may encounter serious issues and risks of failure when faced with …

DepthVision: Robust Vision-Language Understanding through GAN-Based LiDAR-to-RGB Synthesis
Sven Kirchner, Nils Purschke, Ross Greer, Alois C. Knoll
https://arxiv.org/abs/2509.07463

DepthVision: Robust Vision-Language Understanding through GAN-Based LiDAR-to-RGB Synthesis
Ensuring reliable robot operation when visual input is degraded or insufficient remains a central challenge in robotics. This letter introduces DepthVision, a framework for multimodal scene understanding designed to address this problem. Unlike existing Vision-Language Models (VLMs), which use only camera-based visual input alongside language, DepthVision synthesizes RGB images from sparse LiDAR point clouds using a conditional generative adversarial network (GAN) with an integrated refiner net…

Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions
Harrison Field, Max Yang, Yijiong Lin, Efi Psomopoulou, David Barton, Nathan F. Lepora
https://arxiv.org/abs/2509.07445

Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions
Large language models (LLMs) are beginning to automate reward design for dexterous manipulation. However, no prior work has considered tactile sensing, which is known to be critical for human-like dexterity. We present Text2Touch, bringing LLM-crafted rewards to the challenging task of multi-axis in-hand object rotation with real-world vision based tactile sensing in palm-up and palm-down configurations. Our prompt engineering strategy scales to over 70 environment variables, and sim-to-real di…

Timing the Message: Language-Based Notifications for Time-Critical Assistive Settings
Ya-Chuan Hsu, Jonathan DeCastro, Andrew Silva, Guy Rosman
https://arxiv.org/abs/2509.07438 …

Timing the Message: Language-Based Notifications for Time-Critical Assistive Settings
In time-critical settings such as assistive driving, assistants often rely on alerts or haptic signals to prompt rapid human attention, but these cues usually leave humans to interpret situations and decide responses independently, introducing potential delays or ambiguity in meaning. Language-based assistive systems can instead provide instructions backed by context, offering more informative guidance. However, current approaches (e.g., social assistive robots) largely prioritize content gener…

Robust Docking Maneuvers for Autonomous Trolley Collection: An Optimization-Based Visual Servoing Scheme
Yuhan Pang, Bingyi Xia, Zhe Zhang, Zhirui Sun, Peijia Xie, Bike Zhu, Wenjun Xu, Jiankun Wang
https://arxiv.org/abs/2509.07413

Robust Docking Maneuvers for Autonomous Trolley Collection: An Optimization-Based Visual Servoing Scheme
Service robots have demonstrated significant potential for autonomous trolley collection and redistribution in public spaces like airports or warehouses to improve efficiency and reduce cost. Usually, a fully autonomous system for the collection and transportation of multiple trolleys is based on a Leader-Follower formation of mobile manipulators, where reliable docking maneuvers of the mobile base are essential to align trolleys into organized queues. However, developing a vision-based robotic…

Attention and Risk-Aware Decision Framework for Safe Autonomous Driving
Zhen Tian, Fujiang Yuan, Yangfan He, Qinghao Li, Changlin Chen, Huilin Chen, Tianxiang Xu, Jianyu Duan, Yanhong Peng, Zhihao Lin
https://arxiv.org/abs/2509.07412

Attention and Risk-Aware Decision Framework for Safe Autonomous Driving
Autonomous driving has attracted great interest due to its potential capability in full-unsupervised driving. Model-based and learning-based methods are widely used in autonomous driving. Model-based methods rely on pre-defined models of the environment and may struggle with unforeseen events. Proximal policy optimization (PPO), an advanced learning-based method, can adapt to the above limits by learning from interactions with the environment. However, existing PPO faces challenges with poor tr…

TransMPC: Transformer-based Explicit MPC with Variable Prediction Horizon
Sichao Wu, Jiang Wu, Xingyu Cao, Fawang Zhang, Guangyuan Yu, Junjie Zhao, Yue Qu, Fei Ma, Jingliang Duan
https://arxiv.org/abs/2509.07381

TransMPC: Transformer-based Explicit MPC with Variable Prediction Horizon
Traditional online Model Predictive Control (MPC) methods often suffer from excessive computational complexity, limiting their practical deployment. Explicit MPC mitigates online computational load by pre-computing control policies offline; however, existing explicit MPC methods typically rely on simplified system dynamics and cost functions, restricting their accuracy for complex systems. This paper proposes TransMPC, a novel Transformer-based explicit MPC algorithm capable of generating highl…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- Prepared for the Worst: A Learning-Based Adversarial Attack for Resilience Analysis of the ICP Al...
Ziyu Zhang, Johann Laconte, Daniil Lisus, Timothy D. Barfoot

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- Ontological Component-based Description of Robot Capabilities
Bastien Dussard, Guillaume Sarthou, Aur\'elie Clodic

Crosslisted article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[1/1]:
- 3D and 4D World Modeling: A Survey
Lingdong Kong, et al.
https://

RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation
Zongzheng Zhang, Chenghao Yue, Haobo Xu, Minwen Liao, Xianglin Qi, Huan-ang Gao, Ziwei Wang, Hao Zhao
https://arxiv.org/abs/2509.08820

RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation
Robotic chemists promise to both liberate human experts from repetitive tasks and accelerate scientific discovery, yet remain in their infancy. Chemical experiments involve long-horizon procedures over hazardous and deformable substances, where success requires not only task completion but also strict compliance with experimental norms. To address these challenges, we propose \textit{RoboChemist}, a dual-loop framework that integrates Vision-Language Models (VLMs) with Vision-Language-Action (V…

Calib3R: A 3D Foundation Model for Multi-Camera to Robot Calibration and 3D Metric-Scaled Scene Reconstruction
Davide Allegro, Matteo Terreran, Stefano Ghidoni
https://arxiv.org/abs/2509.08813

Calib3R: A 3D Foundation Model for Multi-Camera to Robot Calibration and 3D Metric-Scaled Scene Reconstruction
Robots often rely on RGB images for tasks like manipulation and navigation. However, reliable interaction typically requires a 3D scene representation that is metric-scaled and aligned with the robot reference frame. This depends on accurate camera-to-robot calibration and dense 3D reconstruction, tasks usually treated separately, despite both relying on geometric correspondences from RGB data. Traditional calibration needs patterns, while RGB-based reconstruction yields geometry with an unknow…

Joint Model-based Model-free Diffusion for Planning with Constraints
Wonsuhk Jung, Utkarsh A. Mishra, Nadun Ranawaka Arachchige, Yongxin Chen, Danfei Xu, Shreyas Kousik
https://arxiv.org/abs/2509.08775

Joint Model-based Model-free Diffusion for Planning with Constraints
Model-free diffusion planners have shown great promise for robot motion planning, but practical robotic systems often require combining them with model-based optimization modules to enforce constraints, such as safety. Naively integrating these modules presents compatibility challenges when diffusion's multi-modal outputs behave adversarially to optimization-based modules. To address this, we introduce Joint Model-based Model-free Diffusion (JM2D), a novel generative modeling framework. JM2D fo…

SocialNav-SUB: Benchmarking VLMs for Scene Understanding in Social Robot Navigation
Michael J. Munje, Chen Tang, Shuijing Liu, Zichao Hu, Yifeng Zhu, Jiaxun Cui, Garrett Warnell, Joydeep Biswas, Peter Stone
https://arxiv.org/abs/2509.08757

SocialNav-SUB: Benchmarking VLMs for Scene Understanding in Social Robot Navigation
Robot navigation in dynamic, human-centered environments requires socially-compliant decisions grounded in robust scene understanding. Recent Vision-Language Models (VLMs) exhibit promising capabilities such as object recognition, common-sense reasoning, and contextual understanding-capabilities that align with the nuanced requirements of social robot navigation. However, it remains unclear whether VLMs can accurately understand complex social navigation scenes (e.g., inferring the spatial-temp…

Parallel, Asymptotically Optimal Algorithms for Moving Target Traveling Salesman Problems
Anoop Bhat, Geordan Gutow, Bhaskar Vundurthy, Zhongqiang Ren, Sivakumar Rathinam, Howie Choset
https://arxiv.org/abs/2509.08743

Parallel, Asymptotically Optimal Algorithms for Moving Target Traveling Salesman Problems
The Moving Target Traveling Salesman Problem (MT-TSP) seeks an agent trajectory that intercepts several moving targets, within a particular time window for each target. In the presence of generic nonlinear target trajectories or kinematic constraints on the agent, no prior algorithm guarantees convergence to an optimal MT-TSP solution. Therefore, we introduce the Iterated Random Generalized (IRG) TSP framework. The key idea behind IRG is to alternate between randomly sampling a set of agent con…

TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals
Stefan Podgorski, Sourav Garg, Mehdi Hosseinzadeh, Lachlan Mares, Feras Dayoub, Ian Reid
https://arxiv.org/abs/2509.08699

TANGO: Traversability-Aware Navigation with Local Metric Control for Topological Goals
Visual navigation in robotics traditionally relies on globally-consistent 3D maps or learned controllers, which can be computationally expensive and difficult to generalize across diverse environments. In this work, we present a novel RGB-only, object-level topometric navigation pipeline that enables zero-shot, long-horizon robot navigation without requiring 3D maps or pre-trained controllers. Our approach integrates global topological path planning with local metric trajectory control, allowin…

AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models
Rebecca Martin, Jay Patrikar, Sebastian Scherer
https://arxiv.org/abs/2509.08638 https://

AutoODD: Agentic Audits via Bayesian Red Teaming in Black-Box Models
Specialized machine learning models, regardless of architecture and training, are susceptible to failures in deployment. With their increasing use in high risk situations, the ability to audit these models by determining their operational design domain (ODD) is crucial in ensuring safety and compliance. However, given the high-dimensional input spaces, this process often requires significant human resources and domain expertise. To alleviate this, we introduce \coolname, an LLM-Agent centric fr…

RoboMatch: A Mobile-Manipulation Teleoperation Platform with Auto-Matching Network Architecture for Long-Horizon Manipulation
Hanyu Liu, Yunsheng Ma, Jiaxin Huang, Keqiang Ren, Jiayi Wen, Yilin Zheng, Baishu Wan, Pan Li, Jiejun Hou, Haoru Luan, Zhihua Wang, Zhigong Song
https://arxiv.org/abs/2509.08522…

RoboMatch: A Mobile-Manipulation Teleoperation Platform with Auto-Matching Network Architecture for Long-Horizon Manipulation
This paper presents RoboMatch, a novel unified teleoperation platform for mobile manipulation with an auto-matching network architecture, designed to tackle long-horizon tasks in dynamic environments. Our system enhances teleoperation performance, data collection efficiency, task accuracy, and operational stability. The core of RoboMatch is a cockpit-style control interface that enables synchronous operation of the mobile base and dual arms, significantly improving control precision and data co…

FMT$^{x}$: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
Soheil Espahbodini Nia
https://arxiv.org/abs/2509.08521 https://

FMT$^{x}$: An Efficient and Asymptotically Optimal Extension of the Fast Marching Tree for Dynamic Replanning
Path planning in dynamic environments remains a core challenge in robotics, especially as autonomous systems are deployed in unpredictable spaces such as warehouses and public roads. While algorithms like Fast Marching Tree (FMT$^{*}$) offer asymptotically optimal solutions in static settings, their single-pass design prevents path revisions which are essential for real-time adaptation. On the other hand, full replanning is often too computationally expensive. This paper introduces FMT$^{x}$, a…

Facilitating the Emergence of Assistive Robots to Support Frailty: Psychosocial and Environmental Realities
Angela Higgins, Stephen Potter, Mauro Dragone, Mark Hawley, Farshid Amirabdollahian, Alessandro Di Nuovo, Praminda Caleb-Solly
https://arxiv.org/abs/2509.08510

Facilitating the Emergence of Assistive Robots to Support Frailty: Psychosocial and Environmental Realities
While assistive robots have much potential to help older people with frailty-related needs, there are few in use. There is a gap between what is developed in laboratories and what would be viable in real-world contexts. Through a series of co-design workshops (61 participants across 7 sessions) including those with lived experience of frailty, their carers, and healthcare professionals, we gained a deeper understanding of everyday issues concerning the place of new technologies in their lives. …

CLAP: Clustering to Localize Across n Possibilities, A Simple, Robust Geometric Approach in the Presence of Symmetries
Gabriel I. Fernandez, Ruochen Hou, Alex Xu, Colin Togashi, Dennis W. Hong
https://arxiv.org/abs/2509.08495

CLAP: Clustering to Localize Across n Possibilities, A Simple, Robust Geometric Approach in the Presence of Symmetries
In this paper, we present our localization method called CLAP, Clustering to Localize Across $n$ Possibilities, which helped us win the RoboCup 2024 adult-sized autonomous humanoid soccer competition. Competition rules limited our sensor suite to stereo vision and an inertial sensor, similar to humans. In addition, our robot had to deal with varying lighting conditions, dynamic feature occlusions, noise from high-impact stepping, and mistaken features from bystanders and neighboring fields. The…

Dual-Stage Safe Herding Framework for Adversarial Attacker in Dynamic Environment
Wenqing Wang, Ye Zhang, Haoyu Li, Jingyu Wang
https://arxiv.org/abs/2509.08460 https://

Dual-Stage Safe Herding Framework for Adversarial Attacker in Dynamic Environment
Recent advances in robotics have enabled the widespread deployment of autonomous robotic systems in complex operational environments, presenting both unprecedented opportunities and significant security problems. Traditional shepherding approaches based on fixed formations are often ineffective or risky in urban and obstacle-rich scenarios, especially when facing adversarial agents with unknown and adaptive behaviors. This paper addresses this challenge as an extended herding problem, where def…

Augmenting Neural Networks-based Model Approximators in Robotic Force-tracking Tasks
Kevin Saad, Vincenzo Petrone, Enrico Ferrentino, Pasquale Chiacchio, Francesco Braghin, Loris Roveda
https://arxiv.org/abs/2509.08440

Augmenting Neural Networks-based Model Approximators in Robotic Force-tracking Tasks
As robotics gains popularity, interaction control becomes crucial for ensuring force tracking in manipulator-based tasks. Typically, traditional interaction controllers either require extensive tuning, or demand expert knowledge of the environment, which is often impractical in real-world applications. This work proposes a novel control strategy leveraging Neural Networks (NNs) to enhance the force-tracking behavior of a Direct Force Controller (DFC). Unlike similar previous approaches, it acco…

PegasusFlow: Parallel Rolling-Denoising Score Sampling for Robot Diffusion Planner Flow Matching
Lei Ye, Haibo Gao, Peng Xu, Zhelin Zhang, Junqi Shan, Ao Zhang, Wei Zhang, Ruyi Zhou, Zongquan Deng, Liang Ding
https://arxiv.org/abs/2509.08435

PegasusFlow: Parallel Rolling-Denoising Score Sampling for Robot Diffusion Planner Flow Matching
Diffusion models offer powerful generative capabilities for robot trajectory planning, yet their practical deployment on robots is hindered by a critical bottleneck: a reliance on imitation learning from expert demonstrations. This paradigm is often impractical for specialized robots where data is scarce and creates an inefficient, theoretically suboptimal training pipeline. To overcome this, we introduce PegasusFlow, a hierarchical rolling-denoising framework that enables direct and parallel s…

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…

Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
Sai Puneeth Reddy Gottam, Haoming Zhang, Eivydas Keras
https://arxiv.org/abs/2509.08333

Good Deep Features to Track: Self-Supervised Feature Extraction and Tracking in Visual Odometry
Visual-based localization has made significant progress, yet its performance often drops in large-scale, outdoor, and long-term settings due to factors like lighting changes, dynamic scenes, and low-texture areas. These challenges degrade feature extraction and tracking, which are critical for accurate motion estimation. While learning-based methods such as SuperPoint and SuperGlue show improved feature coverage and robustness, they still face generalization issues with out-of-distribution data…

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities
Rajendramayavan Sathyam, Yueqi Li
https://arxiv.org/abs/2509.08302 https://

Foundation Models for Autonomous Driving Perception: A Survey Through Core Capabilities
Foundation models are revolutionizing autonomous driving perception, transitioning the field from narrow, task-specific deep learning models to versatile, general-purpose architectures trained on vast, diverse datasets. This survey examines how these models address critical challenges in autonomous perception, including limitations in generalization, scalability, and robustness to distributional shifts. The survey introduces a novel taxonomy structured around four essential capabilities for rob…

Symmetry-Guided Multi-Agent Inverse Reinforcement Learnin
Yongkai Tian, Yirong Qi, Xin Yu, Wenjun Wu, Jie Luo
https://arxiv.org/abs/2509.08257 https://arxi…

Symmetry-Guided Multi-Agent Inverse Reinforcement Learnin
In robotic systems, the performance of reinforcement learning depends on the rationality of predefined reward functions. However, manually designed reward functions often lead to policy failures due to inaccuracies. Inverse Reinforcement Learning (IRL) addresses this problem by inferring implicit reward functions from expert demonstrations. Nevertheless, existing methods rely heavily on large amounts of expert demonstrations to accurately recover the reward function. The high cost of collecting…

Behaviorally Heterogeneous Multi-Agent Exploration Using Distributed Task Allocation
Nirabhra Mandal, Aamodh Suresh, Carlos Nieto-Granda, Sonia Mart\'inez
https://arxiv.org/abs/2509.08242

Behaviorally Heterogeneous Multi-Agent Exploration Using Distributed Task Allocation
We study a problem of multi-agent exploration with behaviorally heterogeneous robots. Each robot maps its surroundings using SLAM and identifies a set of areas of interest (AoIs) or frontiers that are the most informative to explore next. The robots assess the utility of going to a frontier using Behavioral Entropy (BE) and then determine which frontier to go to via a distributed task assignment scheme. We convert the task assignment problem into a non-cooperative game and use a distributed alg…

Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates
Zixin Zhang, James Avtges, Todd D. Murphey
https://arxiv.org/abs/2509.08241 https://

Sample-Efficient Online Control Policy Learning with Real-Time Recursive Model Updates
Data-driven control methods need to be sample-efficient and lightweight, especially when data acquisition and computational resources are limited -- such as during learning on hardware. Most modern data-driven methods require large datasets and struggle with real-time updates of models, limiting their performance in dynamic environments. Koopman theory formally represents nonlinear systems as linear models over observables, and Koopman representations can be determined from data in an optimizat…

Deep Visual Odometry for Stereo Event Cameras
Sheng Zhong, Junkai Niu, Yi Zhou
https://arxiv.org/abs/2509.08235 https://arxiv.org/pdf/2509.08235

Deep Visual Odometry for Stereo Event Cameras
Event-based cameras are bio-inspired sensors with pixels that independently and asynchronously respond to brightness changes at microsecond resolution, offering the potential to handle state estimation tasks involving motion blur and high dynamic range (HDR) illumination conditions. However, the versatility of event-based visual odometry (VO) relying on handcrafted data association (either direct or indirect methods) is still unreliable, especially in field robot applications under low-light HD…

Input-gated Bilateral Teleoperation: An Easy-to-implement Force Feedback Teleoperation Method for Low-cost Hardware
Yoshiki Kanai, Akira Kanazawa, Hideyuki Ichiwara, Hiroshi Ito, Naoaki Noguchi, Tetsuya Ogata
https://arxiv.org/abs/2509.08226

Input-gated Bilateral Teleoperation: An Easy-to-implement Force Feedback Teleoperation Method for Low-cost Hardware
Effective data collection in contact-rich manipulation requires force feedback during teleoperation, as accurate perception of contact is crucial for stable control. However, such technology remains uncommon, largely because bilateral teleoperation systems are complex and difficult to implement. To overcome this, we propose a bilateral teleoperation method that relies only on a simple feedback controller and does not require force sensors. The approach is designed for leader-follower setups usi…