Tootfinder

TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization
Zengjue Chen, Runliang Niu, He Kong, Qi Wang
https://arxiv.org/abs/2506.08440

TGRPO :Fine-tuning Vision-Language-Action Model via Trajectory-wise Group Relative Policy Optimization
Recent advances in Vision-Language-Action (VLA) model have demonstrated strong generalization capabilities across diverse scenes, tasks, and robotic platforms when pretrained at large-scale datasets. However, these models still require task-specific fine-tuning in novel environments, a process that relies almost exclusively on supervised fine-tuning (SFT) using static trajectory datasets. Such approaches neither allow robot to interact with environment nor do they leverage feedback from live ex…

"The real threat posed by generative AI is not that it will eliminate work on a mass scale, rendering human labour obsolete. It is that, left unchecked, it will continue to transform work in ways that deepen precarity, intensify surveillance, and widen existing inequalities."
"The current trajectory of generative AI reflects the priorities of firms seeking to lower costs, discipline workers, and consolidate profits — not any drive to enhance human flourishing. If we allo…

Is the AI Bubble About to Burst?
In the years since Automation and the Future of Work first appeared, a new wave of technological enthusiasm has swept across the popular imagination. The catalyst this time has been the rapid advances in generative artificial intelligence, spearheaded by companies like OpenAI, Google DeepMind, and Meta. Once again, a c

Q&A with OpenAI COO Brad Lightcap on GPT-5, its dynamic reasoning, defining AGI, scaling vs. post-training, hallucinations, enterprise adoption, and more (Alex Kantrowitz/Big Technology)
https://www.

OpenAI COO Brad Lightcap: GPT-5's Capabilities, Why It Matters, and Where AI Goes Next
A conversation about what OpenAI's long-anticipated flagship model tells us about different forms intelligence and AI's trajectory from here.

This https://arxiv.org/abs/2501.01619 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

On the pole trajectory of the subthreshold negative parity nucleon with varying pion masses
We study the pole trajectory of the recently established subthreshold negative parity nucleon pole, namely the $N^*(920)$, with varying pion masses, in the scheme of linear $σ$ model with nucleons using the $N/D$ unitarization method. We find that as the pion mass increases, the pole moves toward the real axis. For larger pion masses, at tree level, the pole falls to a specific point on $u$-channel cut and crosses to the adjacent Riemann sheet defined by the logarithmic $u$ channel cut. At one…

SCREP: Scene Coordinate Regression and Evidential Learning-based Perception-Aware Trajectory Generation
Juyeop Han, Lukas Lao Beyer, Guilherme V. Cavalheiro, Sertac Karaman
https://arxiv.org/abs/2507.07467

SCREP: Scene Coordinate Regression and Evidential Learning-based Perception-Aware Trajectory Generation
Autonomous flight in GPS denied indoor spaces requires trajectories that keep visual localization error tightly bounded across varied missions. Whereas visual inertial odometry (VIO) accumulates drift over time, scene coordinate regression (SCR) yields drift-free, high accuracy absolute pose estimation. We present a perception-aware framework that couples an evidential learning-based SCR pose estimator with a receding horizon trajectory optimizer. The optimizer steers the onboard camera toward …

Direct Evidence Of Apex Hypha Interactions During Vegetative Growth Of Fungal Thallus Via Comprehensive Network And Trajectory Extraction
Thibault Chassereau, Florence Leclerc, Eric Herbert
https://arxiv.org/abs/2507.06280

Direct Evidence Of Apex Hypha Interactions During Vegetative Growth Of Fungal Thallus Via Comprehensive Network And Trajectory Extraction
The mycelium of a filamentous fungus is a growing, branching network of numerous entangled hyphae exhibiting polarised apical growth. Expansion occurs during the vegetative phase from a single ascospore, driven by the need to explore and occupy surrounding space limiting competitors, enhancing nutrient uptake, and promoting spore dispersal. Radial, rapid, and rectilinear growth combined with frequent branching appears adaptive. However, passive growth without interactions or feedback may produc…

Joint Routing and Control Optimization in VANET
Chen Huang, Dingxuan Wang, Ronghui Hou
https://arxiv.org/abs/2506.08038 https://arxiv…

Joint Routing and Control Optimization in VANET
In this paper, we introduce DynaRoute, an adaptive joint optimization framework for dynamic vehicular networks that simultaneously addresses platoon control and data transmission through trajectory-aware routing and safety-constrained vehicle coordination. DynaRoute guarantees continuous vehicle movement via platoon safety control with optimizing transmission paths through real-time trajectory prediction and ensuring reliable data. Our solution achieves three key objectives: (1) maintaining pla…

Here's my latest Boles Bells article!
https://bolesbells.com/blog/2025/6/9/the-iron-ballast-kettlebells-national-identity-and-the-russian-mythos-of-strength

The Iron Ballast: Kettlebells, National Identity, and the Russian Mythos of Strength — David Boles Bells
The trajectory of Russia over the past century is a story deeply etched with societal upheavals, ideological fervor, and an unyielding quest for national assertion on the global stage. Knotted into this complex tapestry is the seemingly modest history of the kettlebell, or girya . This unassuming i

Replaced article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
[1/1]:
- One Trajectory, One Token: Grounded Video Tokenization via Panoptic Sub-object Trajectory
Zheng, Zhang, Salehi, Gao, Iyengar, Kobori, Kong, Krishna

Analysis of Atomic Charge State and Atomic Number for VAMOS Magnetic Spectrometer using Deep Neural Networks and Fractionally Labelled Events
M. Rejmund, A. Lemasson
https://arxiv.org/abs/2507.07109

Analysis of Atomic Charge State and Atomic Number for VAMOS++ Magnetic Spectrometer using Deep Neural Networks and Fractionally Labelled Events
The VAMOS++ magnetic spectrometer is a multi-parametric system that integrates ion optical magnetic elements with a multi-detector stack. The magnetic elements, along with the tracking and timing detectors and the trajectory reconstruction method, provide the analysis of the magnetic rigidity, the trajectory length between the beam interaction point and the focal plane of the spectrometer, and the related velocity and mass-over-charge ratio. The segmented ionization chamber provides the energy …

Cowboys UDFA could be on former star's trajectory https://insidethestar.com/cowboys-udfa-could-be-on-former-stars-trajectory

This https://arxiv.org/abs/2410.22931 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

A Third-Order Gaussian Process Trajectory Representation Framework with Closed-Form Kinematics for Continuous-Time Motion Estimation
In this paper, we propose a third-order, i.e., white-noise-on-jerk, Gaussian Process (GP) Trajectory Representation (TR) framework for continuous-time (CT) motion estimation (ME) tasks. Our framework features a unified trajectory representation that encapsulates the kinematic models of both $SO(3)\times\mathbb{R}^3$ and $SE(3)$ pose representations. This encapsulation strategy allows users to use the same implementation of measurement-based factors for either choice of pose representation, whic…

This https://arxiv.org/abs/2502.01907 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Robust Cislunar Low-Thrust Trajectory Optimization under Uncertainties via Sequential Covariance Steering
Spacecraft operations are influenced by uncertainties such as dynamics modeling, navigation, and maneuver execution errors. Although mission design has traditionally incorporated heuristic safety margins to mitigate the effect of uncertainties, particularly before/after crucial events, it is yet unclear whether this practice will scale in the cislunar region, which features locally chaotic nonlinear dynamics and involves frequent lunar flybys. This paper applies chance-constrained covariance st…

VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
Juyi Lin, Amir Taherin, Arash Akbari, Arman Akbari, Lei Lu, Guangyu Chen, Taskin Padir, Xiaomeng Yang, Weiwei Chen, Yiqian Li, Xue Lin, David Kaeli, Pu Zhao, Yanzhi Wang
https://arxiv.org/abs/2507.05116

VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
Recent large-scale Vision Language Action (VLA) models have shown superior performance in robotic manipulation tasks guided by natural language. However, their generalization remains limited when applied to novel objects or unfamiliar environments that lie outside the training distribution. To address this, many existing approaches integrate additional components such as depth estimation, segmentation, or even diffusion to improve generalization, at the cost of adding significant computation ov…

Trace and Trajectory in East Asian Cultures Conference
https://ift.tt/dB7ILmU
updated: Tuesday, August 5, 2025 - 2:35pmfull name / name of organization: The 3rd Graduate Student…
via Input 4 RELCFP

This https://arxiv.org/abs/2410.03815 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Single-Shot Learning of Multirotor Controller Gains: A Data-Driven Approach with Experimental Validation
This paper demonstrates the single-shot learning capabilities of retrospective cost optimization based data-driven control applied to learning multirotor controller gains for trajectory tracking. In particular, the proposed control approach is first used within a simple multirotor simulation environment to learn appropriate multirotor controller gains to follow a trajectory. Then, the gains resulting from a single simulation run are used in a more complex multirotor simulation environment based…

Series A, Episode 09 - Project Avalon
BLAKE: [V.O.] Can you identify it?
GAN: Well, the detectors read it as a military command cruiser and the trajectory suggested it could have come from Federation space headquarters.
https://blake.torpidity.net/m/109/143 B7B2

Optimal $C^{\frac{1}{2}}$ regularity of the Boltzmann equation in non-convex domains
Gayoung An, Donghyun Lee
https://arxiv.org/abs/2507.07353 https://

Optimal $C^{\frac{1}{2}}$ regularity of the Boltzmann equation in non-convex domains
Regularity of the Boltzmann equation, particularly in the presence of physical boundary conditions, heavily relies on the geometry of the boundaries. In the case of non-convex domains with specular reflection boundary conditions, the problem remained outstanding until recently due to the severe singularity of billiard trajectories near the grazing set, where the trajectory map is not differentiable. This challenge was addressed in [32], where $C^{\frac{1}{2}-}_{x,v}$ Hölder regularity was prov…

Reusing Trajectories in Policy Gradients Enables Fast Convergence
Alessandro Montenegro, Federico Mansutti, Marco Mussi, Matteo Papini, Alberto Maria Metelli
https://arxiv.org/abs/2506.06178

Reusing Trajectories in Policy Gradients Enables Fast Convergence
Policy gradient (PG) methods are a class of effective reinforcement learning algorithms, particularly when dealing with continuous control problems. These methods learn the parameters of parametric policies via stochastic gradient ascent, typically using on-policy trajectory data to estimate the policy gradient. However, such reliance on fresh data makes them sample-inefficient. Indeed, vanilla PG methods require $O(ε^{-2})$ trajectories to reach an $ε$-approximate stationary point. A common …

This https://arxiv.org/abs/2201.10300 has been replaced.
link: https://scholar.google.com/scholar?q=a

The Inverse Problem for Single Trajectories of Rough Differential Equations
Motivated by the need to develop a general framework for performing statistical inference for discretely observed random rough differential equations, our aim is to construct a geometric $p$-rough path ${\bf X}$ whose response $Y$, when driving a rough differential equation, matches the observed trajectory $y$. We call this the \textit{continuous inverse problem} and start by rigorously defining its solution. We then develop a framework where the solution can be constructed as a limit of soluti…

Periodic Bipedal Gait Learning Using Reward Composition Based on a Novel Gait Planner for Humanoid Robots
Bolin Li, Linwei Sun, Xuecong Huang, Yuzhi Jiang, Lijun Zhu
https://arxiv.org/abs/2506.08416

Periodic Bipedal Gait Learning Using Reward Composition Based on a Novel Gait Planner for Humanoid Robots
This paper presents a periodic bipedal gait learning method using reward composition, integrated with a real-time gait planner for humanoid robots. First, we introduce a novel gait planner that incorporates dynamics to design the desired joint trajectory. In the gait design process, the 3D robot model is decoupled into two 2D models, which are then approximated as hybrid inverted pendulums (H-LIP) for trajectory planning. The gait planner operates in parallel in real time within the robot's lea…

The @… paper Algorithmic Alphabets for Patterns of Movement by @… @… me is published !

GTRSS: Graph-based Top-$k$ Representative Similar Subtrajectory Query
Mingchang Ge, Liping Wang, Xuemin Lin, Yuang Zhang, Kunming Wang
https://arxiv.org/abs/2507.05542

GTRSS: Graph-based Top-$k$ Representative Similar Subtrajectory Query
Trajectory mining has attracted significant attention. This paper addresses the Top-k Representative Similar Subtrajectory Query (TRSSQ) problem, which aims to find the k most representative subtrajectories similar to a query. Existing methods rely on costly filtering-validation frameworks, resulting in slow response times. Addressing this, we propose GTRSS, a novel Graph-based Top-k Representative Similar Subtrajectory Query framework. During the offline phase, GTRSS builds a dual-layer graph …

From Marginal to Joint Predictions: Evaluating Scene-Consistent Trajectory Prediction Approaches for Automated Driving
Fabian Konstantinidis, Ariel Dallari Guerreiro, Raphael Trumpp, Moritz Sackmann, Ulrich Hofmann, Marco Caccamo, Christoph Stiller
https://arxiv.org/abs/2507.05254

From Marginal to Joint Predictions: Evaluating Scene-Consistent Trajectory Prediction Approaches for Automated Driving
Accurate motion prediction of surrounding traffic participants is crucial for the safe and efficient operation of automated vehicles in dynamic environments. Marginal prediction models commonly forecast each agent's future trajectories independently, often leading to sub-optimal planning decisions for an automated vehicle. In contrast, joint prediction models explicitly account for the interactions between agents, yielding socially and physically consistent predictions on a scene level. However…

Trajectory Optimization for UAV-Based Medical Delivery with Temporal Logic Constraints and Convex Feasible Set Collision Avoidance
Kaiyuan Chen, Yuhan Suo, Shaowei Cui, Yuanqing Xia, Wannian Liang, Shuo Wang
https://arxiv.org/abs/2506.06038

Trajectory Optimization for UAV-Based Medical Delivery with Temporal Logic Constraints and Convex Feasible Set Collision Avoidance
This paper addresses the problem of trajectory optimization for unmanned aerial vehicles (UAVs) performing time-sensitive medical deliveries in urban environments. Specifically, we consider a single UAV with 3 degree-of-freedom dynamics tasked with delivering blood packages to multiple hospitals, each with a predefined time window and priority. Mission objectives are encoded using Signal Temporal Logic (STL), enabling the formal specification of spatial-temporal constraints. To ensure safety, c…

Fusion of heterogeneous data for robust degradation prognostics
Edgar Jaber (EDF R\&D PRISME, CB, DATAFLOT), Emmanuel Remy (EDF R\&D PRISME), Vincent Chabridon (EDF R\&D PRISME), Mathilde Mougeot (ENSIIE, CB), Didier Lucor (DATAFLOT)
https://arxiv.org/abs/2506.05882

Fusion of heterogeneous data for robust degradation prognostics
Assessing the degradation state of an industrial asset first requires evaluating its current condition and then to project the forecast model trajectory to a predefined prognostic threshold, thereby estimating its remaining useful life (RUL). Depending on the available information, two primary categories of forecasting models may be used: physics-based simulation codes and datadriven (machine learning) approaches. Combining both modelling approaches may enhance prediction robustness, especially…

TransAM: Transformer-Based Agent Modeling for Multi-Agent Systems via Local Trajectory Encoding
Conor Wallace, Umer Siddique, Yongcan Cao
https://arxiv.org/abs/2508.02826 https:…

TransAM: Transformer-Based Agent Modeling for Multi-Agent Systems via Local Trajectory Encoding
Agent modeling is a critical component in developing effective policies within multi-agent systems, as it enables agents to form beliefs about the behaviors, intentions, and competencies of others. Many existing approaches assume access to other agents' episodic trajectories, a condition often unrealistic in real-world applications. Consequently, a practical agent modeling approach must learn a robust representation of the policies of the other agents based only on the local trajectory of the c…

Trace and Trajectory in East Asian Cultures Conference
https://ift.tt/dB7ILmU
updated: Tuesday, August 5, 2025 - 2:35pmfull name / name of organization: The 3rd Graduate Student…
via Input 4 RELCFP

Composite Hybrid Inflation : Primordial Black Holes and Stochastic Gravitational Waves
Giacomo Cacciapaglia, Dhong Yeon Cheong, Aldo Deandrea, Wanda Isnard, Seong Chan Park, Xinpeng Wang, Ying-li Zhang
https://arxiv.org/abs/2506.06655

Composite Hybrid Inflation : Primordial Black Holes and Stochastic Gravitational Waves
We investigate the production of primordial black holes and gravitational waves in composite hybrid inflation. Starting from an effective chiral Lagrangian with a dilaton and pions, we identify inflation occurring due to the walking dynamics of the theory. A $\mathbb{Z}_2$ symmetry-breaking term in the pion sector induces a shift in the inflaton's trajectory, which leads to a tachyonic instability phase. Curvature perturbations grow exponentially, producing copious primordial black holes and a …

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

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

Search for Sub-Relativistic Magnetic Monopoles with the IceCube Neutrino Observatory
Jonas H\"au{\ss}ler (for the IceCube Collaboration), Nicolas Moller (for the IceCube Collaboration)
https://arxiv.org/abs/2507.05896

Search for Sub-Relativistic Magnetic Monopoles with the IceCube Neutrino Observatory
Magnetic monopoles are beyond standard model particles, predicted by Grand Unified Theories (GUTs) to be created during the early universe. At typical masses of the GUT-scale - above $10^{14}$ GeV - these particles would move at sub-relativistic speeds. The Rubakov-Callan effect predicts that magnetic monopoles can catalyze nucleon decays, in particular the decay of protons. This results in a unique signature of small particle cascades along the trajectory of the slow moving magnetic monopole. …

UAV-UGV Cooperative Trajectory Optimization and Task Allocation for Medical Rescue Tasks in Post-Disaster Environments
Kaiyuan Chen, Wanpeng Zhao, Yongxi Liu, Yuanqing Xia, Wannian Liang, Shuo Wang
https://arxiv.org/abs/2506.06136

UAV-UGV Cooperative Trajectory Optimization and Task Allocation for Medical Rescue Tasks in Post-Disaster Environments
In post-disaster scenarios, rapid and efficient delivery of medical resources is critical and challenging due to severe damage to infrastructure. To provide an optimized solution, we propose a cooperative trajectory optimization and task allocation framework leveraging unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). This study integrates a Genetic Algorithm (GA) for efficient task allocation among multiple UAVs and UGVs, and employs an informed-RRT* (Rapidly-exploring Rando…

Online Competitive Information Gathering for Partially Observable Trajectory Games
Mel Krusniak, Hang Xu, Parker Palermo, Forrest Laine
https://arxiv.org/abs/2506.01927

Online Competitive Information Gathering for Partially Observable Trajectory Games
Game-theoretic agents must make plans that optimally gather information about their opponents. These problems are modeled by partially observable stochastic games (POSGs), but planning in fully continuous POSGs is intractable without heavy offline computation or assumptions on the order of belief maintained by each player. We formulate a finite history/horizon refinement of POSGs which admits competitive information gathering behavior in trajectory space, and through a series of approximations,…

SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation
Jiahao Zhu, Zixuan Chen, Guangcong Wang, Xiaohua Xie, Yi Zhou
https://arxiv.org/abs/2507.05256

SegmentDreamer: Towards High-fidelity Text-to-3D Synthesis with Segmented Consistency Trajectory Distillation
Recent advancements in text-to-3D generation improve the visual quality of Score Distillation Sampling (SDS) and its variants by directly connecting Consistency Distillation (CD) to score distillation. However, due to the imbalance between self-consistency and cross-consistency, these CD-based methods inherently suffer from improper conditional guidance, leading to sub-optimal generation results. To address this issue, we present SegmentDreamer, a novel framework designed to fully unleash the p…

Lagrangian-based Equilibrium Propagation: generalisation to arbitrary boundary conditions & equivalence with Hamiltonian Echo Learning
Guillaume Pourcel, Debabrota Basu, Maxence Ernoult, Aditya Gilra
https://arxiv.org/abs/2506.06248

Lagrangian-based Equilibrium Propagation: generalisation to arbitrary boundary conditions & equivalence with Hamiltonian Echo Learning
Equilibrium Propagation (EP) is a learning algorithm for training Energy-based Models (EBMs) on static inputs which leverages the variational description of their fixed points. Extending EP to time-varying inputs is a challenging problem, as the variational description must apply to the entire system trajectory rather than just fixed points, and careful consideration of boundary conditions becomes essential. In this work, we present Generalized Lagrangian Equilibrium Propagation (GLEP), which e…

Global Optimality in Multi-Flyby Asteroid Trajectory Optimization: Theory and Application Techniques
Zhong Zhang, Xiang Guo, Di Wu, Hexi Baoyin, Junfeng Li, Francesco Topputo
https://arxiv.org/abs/2508.02904

Global Optimality in Multi-Flyby Asteroid Trajectory Optimization: Theory and Application Techniques
Designing optimal trajectories for multi-flyby asteroid missions is scientifically critical but technically challenging due to nonlinear dynamics, intermediate constraints, and numerous local optima. This paper establishes a method that approaches global optimality for multi-flyby trajectory optimization under a given sequence. The original optimal control problem with interior-point equality constraints is transformed into a multi-stage decision formulation. This reformulation enables direct a…

Quantum vortex dipole as a probe of the normal component distribution
Andrea Barresi, Piotr Magierski, Gabriel Wlaz{\l}owski
https://arxiv.org/abs/2507.05817

Quantum vortex dipole as a probe of the normal component distribution
We investigate the dynamics of quantum vortex dipoles in a strongly interacting, spin-imbalanced Fermi superfluid at zero temperature. Using fully microscopic time-dependent density functional theory, we demonstrate that the dipole trajectory is strongly influenced by the spatial distribution of spin polarization. The resulting forces on the vortices include both longitudinal (dissipative) and transverse components, leading to deflection and shrinking of the dipole during propagation. For moder…

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

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

Energy Efficient Trajectory Control and Resource Allocation in Multi-UAV-assisted MEC via Deep Reinforcement Learning
Saichao Liu, Geng Sun, Chuang Zhang, Xuejie Liu, Jiacheng Wang, Changyuan Zhao, Dusit Niyato
https://arxiv.org/abs/2508.00261

Energy Efficient Trajectory Control and Resource Allocation in Multi-UAV-assisted MEC via Deep Reinforcement Learning
Mobile edge computing (MEC) is a promising technique to improve the computational capacity of smart devices (SDs) in Internet of Things (IoT). However, the performance of MEC is restricted due to its fixed location and limited service scope. Hence, we investigate an unmanned aerial vehicle (UAV)-assisted MEC system, where multiple UAVs are dispatched and each UAV can simultaneously provide computing service for multiple SDs. To improve the performance of system, we formulated a UAV-based trajec…

A visual analytics tool for taxonomy-based trajectory data exploration
Ivan A. Hanono Cozzetti, Ahmad Abdou
https://arxiv.org/abs/2507.22899 https://arxiv.…

A visual analytics tool for taxonomy-based trajectory data exploration
The analysis of spatio-temporal data presents significant challenges due to the complexity and heterogeneity of movement patterns. This project proposes a data analytics tool that combines data visualization and statistical computation to facilitate spatio-temporal data analysis through a multi-level approach. The tool categorizes moving objects into distinct taxonomies using Machine Learning models, adding meaningful structure to the analysis. Two case studies demonstrate the methodology's eff…

Risk-Aware Trajectory Optimization and Control for an Underwater Suspended Robotic System
Yuki Origane, Nicolas Hoischen, Tzu-Yuan Huang, Daisuke Kurabayashi, Stefan Sosnowski, Sandra Hirche
https://arxiv.org/abs/2507.04640

Risk-Aware Trajectory Optimization and Control for an Underwater Suspended Robotic System
This paper focuses on the trajectory optimization of an underwater suspended robotic system comprising an uncrewed surface vessel (USV) and an uncrewed underwater vehicle (UUV) for autonomous litter collection. The key challenge lies in the significant uncertainty in drag and weight parameters introduced by the collected litter. We propose a dynamical model for the coupled UUV-USV system in the primary plane of motion and a risk-aware optimization approach incorporating parameter uncertainty an…

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

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

Robust One-step Speech Enhancement via Consistency Distillation
Liang Xu, Longfei Felix Yan, W. Bastiaan Kleijn
https://arxiv.org/abs/2507.05688 https://…

Robust One-step Speech Enhancement via Consistency Distillation
Diffusion models have shown strong performance in speech enhancement, but their real-time applicability has been limited by multi-step iterative sampling. Consistency distillation has recently emerged as a promising alternative by distilling a one-step consistency model from a multi-step diffusion-based teacher model. However, distilled consistency models are inherently biased towards the sampling trajectory of the teacher model, making them less robust to noise and prone to inheriting inaccura…

Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition
Pulkit Kumar, Shuaiyi Huang, Matthew Walmer, Sai Saketh Rambhatla, Abhinav Shrivastava
https://arxiv.org/abs/2508.03695

Trokens: Semantic-Aware Relational Trajectory Tokens for Few-Shot Action Recognition
Video understanding requires effective modeling of both motion and appearance information, particularly for few-shot action recognition. While recent advances in point tracking have been shown to improve few-shot action recognition, two fundamental challenges persist: selecting informative points to track and effectively modeling their motion patterns. We present Trokens, a novel approach that transforms trajectory points into semantic-aware relational tokens for action recognition. First, we i…

Nonlocal Mechanics
Carlos Heredia, Josep Llosa
https://arxiv.org/abs/2508.03601 https://arxiv.org/pdf/2508.03601 …

Nonlocal Mechanics
We introduce a Hamiltonian framework for nonlocal Lagrangian systems without relying on infinite-derivative expansions. Starting from a (trajectory-based) variational principle and a generalized Noether theorem, we define the canonical momenta and energy. Moreover, we construct a (pre)symplectic form on the kinematic space, and show that its restriction to the phase space (by implementing the constraints) yields a true (pre)symplectic structure encoding the dynamics. Three examples -- a finite …

Controllable Surface Diffusion Generative Model for Neurodevelopmental Trajectories
Zhenshan Xie, Levente Baljer, M. Jorge Cardoso, Emma Robinson
https://arxiv.org/abs/2508.03706

Controllable Surface Diffusion Generative Model for Neurodevelopmental Trajectories
Preterm birth disrupts the typical trajectory of cortical neurodevelopment, increasing the risk of cognitive and behavioral difficulties. However, outcomes vary widely, posing a significant challenge for early prediction. To address this, individualized simulation offers a promising solution by modeling subject-specific neurodevelopmental trajectories, enabling the identification of subtle deviations from normative patterns that might act as biomarkers of risk. While generative models have show…

Hi friends,
I’ve just made a five-track release available - 'Music for Planetariums':
https://robertloganmusic.bandcamp.com/album/music-for-planetariums
These pieces come from a trajectory of very minimal music I love to make - whe…

TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis
Chunhou Ji, Qiumeng Li
https://arxiv.org/abs/2506.16401 https://

TrajSceneLLM: A Multimodal Perspective on Semantic GPS Trajectory Analysis
GPS trajectory data reveals valuable patterns of human mobility and urban dynamics, supporting a variety of spatial applications. However, traditional methods often struggle to extract deep semantic representations and incorporate contextual map information. We propose TrajSceneLLM, a multimodal perspective for enhancing semantic understanding of GPS trajectories. The framework integrates visualized map images (encoding spatial context) and textual descriptions generated through LLM reasoning (…

REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transport
Farzad Beizaee, Sina Hajimiri, Ismail Ben Ayed, Gregory Lodygensky, Christian Desrosiers, Jose Dolz
https://arxiv.org/abs/2508.02889

REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transport
Unsupervised anomaly detection (UAD) in brain imaging is crucial for identifying pathologies without the need for labeled data. However, accurately localizing anomalies remains challenging due to the intricate structure of brain anatomy and the scarcity of abnormal examples. In this work, we introduce REFLECT, a novel framework that leverages rectified flows to establish a direct, linear trajectory for correcting abnormal MR images toward a normal distribution. By learning a straight, one-step …

Dynamics of singularly perturbed sliding flow in Filippov systems
Piotr Kowalczyk, Jan Sieber
https://arxiv.org/abs/2508.03942 https://arxiv.org/pdf/2508.0…

Dynamics of singularly perturbed sliding flow in Filippov systems
In this article, we present an analysis of the effects of singular perturbations on the sliding motion in Filippov systems. We show that singular perturbations may lead to qualitatively distinct topologies of phase space on the switching manifold, which we classify into six distinct topologies. Five of these topologies imply that singularly perturbed trajectory includes a segment (or segments) of sliding, and one topology, which we study here, implies the evolution characterised by switchings b…

Robust adaptive fuzzy sliding mode control for trajectory tracking for of cylindrical manipulator
Van Cuong Pham, Minh Hai Tran, Phuc Anh Nguyen, Ngoc Son Vu, Nga Nguyen Thi
https://arxiv.org/abs/2508.05584

Robust adaptive fuzzy sliding mode control for trajectory tracking for of cylindrical manipulator
This research proposes a robust adaptive fuzzy sliding mode control (AFSMC) approach to enhance the trajectory tracking performance of cylindrical robotic manipulators, extensively utilized in applications such as CNC and 3D printing. The proposed approach integrates fuzzy logic with sliding mode control (SMC) to bolster adaptability and robustness, with fuzzy logic approximating the uncertain dynamics of the system, while SMC ensures strong performance. Simulation results in MATLAB/Simulink de…

IKMo: Image-Keyframed Motion Generation with Trajectory-Pose Conditioned Motion Diffusion Model
Yang Zhao, Yan Zhang, Xubo Yang
https://arxiv.org/abs/2505.21146

IKMo: Image-Keyframed Motion Generation with Trajectory-Pose Conditioned Motion Diffusion Model
Existing human motion generation methods with trajectory and pose inputs operate global processing on both modalities, leading to suboptimal outputs. In this paper, we propose IKMo, an image-keyframed motion generation method based on the diffusion model with trajectory and pose being decoupled. The trajectory and pose inputs go through a two-stage conditioning framework. In the first stage, the dedicated optimization module is applied to refine inputs. In the second stage, trajectory and pose …

This https://arxiv.org/abs/2505.24434 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Graph Flow Matching: Enhancing Image Generation with Neighbor-Aware Flow Fields
Flow matching casts sample generation as learning a continuous-time velocity field that transports noise to data. Existing flow matching networks typically predict each point's velocity independently, considering only its location and time along its flow trajectory, and ignoring neighboring points. However, this pointwise approach may overlook correlations between points along the generation trajectory that could enhance velocity predictions, thereby improving downstream generation quality. To …

VLM-TDP: VLM-guided Trajectory-conditioned Diffusion Policy for Robust Long-Horizon Manipulation
Kefeng Huang, Tingguang Li, Yuzhen Liu, Zhe Zhang, Jiankun Wang, Lei Han
https://arxiv.org/abs/2507.04524

VLM-TDP: VLM-guided Trajectory-conditioned Diffusion Policy for Robust Long-Horizon Manipulation
Diffusion policy has demonstrated promising performance in the field of robotic manipulation. However, its effectiveness has been primarily limited in short-horizon tasks, and its performance significantly degrades in the presence of image noise. To address these limitations, we propose a VLM-guided trajectory-conditioned diffusion policy (VLM-TDP) for robust and long-horizon manipulation. Specifically, the proposed method leverages state-of-the-art vision-language models (VLMs) to decompose lo…

Replaced article(s) found for cs.CV. https://arxiv.org/list/cs.CV/new
[3/5]:
- GC-GAT: Multimodal Vehicular Trajectory Prediction using Graph Goal Conditioning and Cross-contex...
Mahir Gulzar, Yar Muhammad, Naveed Muhammad

Optimizing Age of Trust and Throughput in Multi-Hop UAV-Aided IoT Networks
Yizhou Luo, Kwan-Wu Chin, Ruyi Guan, Xi Xiao, Caimeng Wang, Jingyin Feng, Tengjiao He
https://arxiv.org/abs/2507.03950

Optimizing Age of Trust and Throughput in Multi-Hop UAV-Aided IoT Networks
Devices operating in Internet of Things (IoT) networks may be deployed across vast geographical areas and interconnected via multi-hop communications. Further, they may be unguarded. This makes them vulnerable to attacks and motivates operators to check on devices frequently. To this end, we propose and study an Unmanned Aerial Vehicle (UAV)-aided attestation framework for use in IoT networks with a charging station powered by solar. A key challenge is optimizing the trajectory of the UAV to en…

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

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

Control Synthesis Along Uncertain Trajectories Using Integral Quadratic Constraints
Felix Biert\"umpfel, Peter Seiler, Harald Pfifer
https://arxiv.org/abs/2507.03101

Control Synthesis Along Uncertain Trajectories Using Integral Quadratic Constraints
The paper presents a novel approach to synthesize robust controllers for nonlinear systems along perturbed trajectories. The approach linearizes the system with respect to a reference trajectory. In contrast to existing methods rooted in robust linear time-varying synthesis, the approach accurately includes perturbations that drive the system away from the reference trajectory. Hence, the controller obtained in the linear framework provides a significantly more robust nonlinear performance. The…

Energy-Efficient Secure Communications via Joint Optimization of UAV Trajectory and Movable-Antenna Array Beamforming
Sanghyeok Kim, Jinu Gong, Joonhyuk Kang
https://arxiv.org/abs/2507.20489

Energy-Efficient Secure Communications via Joint Optimization of UAV Trajectory and Movable-Antenna Array Beamforming
This paper investigates the potential of unmanned aerial vehicles (UAVs) equipped with movable-antenna (MA) arrays to strengthen security in wireless communication systems. We propose a novel framework that jointly optimizes the UAV trajectory and the reconfigurable beamforming of the MA array to maximize secrecy energy efficiency, while ensuring reliable communication with legitimate users. By exploiting the spatial degrees of freedom enabled by the MA array, the system can form highly directi…

Cowboys Headlines: Travis Frederick honored; news on former DE's arrest, longtime OL dies https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2025/06/06/news-headlines-june-5-…

MapAgent: Trajectory-Constructed Memory-Augmented Planning for Mobile Task Automation
Yi Kong, Dianxi Shi, Guoli Yang, Zhang ke-di, Chenlin Huang, Xiaopeng Li, Songchang Jin
https://arxiv.org/abs/2507.21953

MapAgent: Trajectory-Constructed Memory-Augmented Planning for Mobile Task Automation
The recent advancement of autonomous agents powered by Large Language Models (LLMs) has demonstrated significant potential for automating tasks on mobile devices through graphical user interfaces (GUIs). Despite initial progress, these agents still face challenges when handling complex real-world tasks. These challenges arise from a lack of knowledge about real-life mobile applications in LLM-based agents, which may lead to ineffective task planning and even cause hallucinations. To address the…

WaMo: Wavelet-Enhanced Multi-Frequency Trajectory Analysis for Fine-Grained Text-Motion Retrieval
Junlong Ren, Gangjian Zhang, Honghao Fu, Pengcheng Wu, Hao Wang
https://arxiv.org/abs/2508.03343

WaMo: Wavelet-Enhanced Multi-Frequency Trajectory Analysis for Fine-Grained Text-Motion Retrieval
Text-Motion Retrieval (TMR) aims to retrieve 3D motion sequences semantically relevant to text descriptions. However, matching 3D motions with text remains highly challenging, primarily due to the intricate structure of human body and its spatial-temporal dynamics. Existing approaches often overlook these complexities, relying on general encoding methods that fail to distinguish different body parts and their dynamics, limiting precise semantic alignment. To address this, we propose WaMo, a nov…

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

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

CRED: Counterfactual Reasoning and Environment Design for Active Preference Learning
Yi-Shiuan Tung, Bradley Hayes, Alessandro Roncone
https://arxiv.org/abs/2507.05458

CRED: Counterfactual Reasoning and Environment Design for Active Preference Learning
For effective real-world deployment, robots should adapt to human preferences, such as balancing distance, time, and safety in delivery routing. Active preference learning (APL) learns human reward functions by presenting trajectories for ranking. However, existing methods often struggle to explore the full trajectory space and fail to identify informative queries, particularly in long-horizon tasks. We propose CRED, a trajectory generation method for APL that improves reward estimation by join…

$\lambda$ and $\rho$ Regge trajectories for the pentaquark $P_{cc\bar{c}bb}$ in the diquark-triquark picture
He Song, Xin-Ru Liu, Jia-Qi Xie, Jiao-Kai Chen
https://arxiv.org/abs/2506.01005

$λ$ and $ρ$ Regge trajectories for the pentaquark $P_{cc\bar{c}bb}$ in the diquark-triquark picture
We propose the Regge trajectory relations for the fully heavy pentaquark $P_{cc\bar{c}bb}$ utilizing both diquark and triquark Regge trajectory relations. Using these new relations, we discuss four series of Regge trajectories: the $ρ_1$-, $ρ_2$-, $λ_1$-, and $λ_2$-trajectories. We provide rough estimates for the masses of the $ρ_1$-, $ρ_2$-, $λ_1$-, and $λ_2$-excited states. Except for the $λ_1$-trajectories, the complete forms of the other three series of Regge trajectories for the p…

Risk-Aware Aerocapture Guidance Through a Probabilistic Indicator Function
Grace E. Calkins, Jay W. McMahon, Alireza Doostan, David C. Woffinden
https://arxiv.org/abs/2507.05454

Risk-Aware Aerocapture Guidance Through a Probabilistic Indicator Function
Aerocapture is sensitive to trajectory errors, particularly for low-cost missions with imprecise navigation. For such missions, considering the probability of each failure mode when computing guidance commands can increase performance. A risk-aware aerocapture guidance algorithm is proposed that uses a generative-modeling-based probabilistic indicator function to estimate escape, impact, or capture probabilities. The probability of each mode is incorporated into corrective guidance commands to …

Probabilistic Trajectory GOSPA: A Metric for Uncertainty-Aware Multi-Object Tracking Performance Evaluation
Yuxuan Xia, \'Angel F. Garc\'ia-Fern\'andez, Johan Karlsson, Yu Ge, Lennart Svensson, Ting Yuan
https://arxiv.org/abs/2506.15148

Probabilistic Trajectory GOSPA: A Metric for Uncertainty-Aware Multi-Object Tracking Performance Evaluation
This paper presents a generalization of the trajectory general optimal sub-pattern assignment (GOSPA) metric for evaluating multi-object tracking algorithms that provide trajectory estimates with track-level uncertainties. This metric builds on the recently introduced probabilistic GOSPA metric to account for both the existence and state estimation uncertainties of individual object states. Similar to trajectory GOSPA (TGOSPA), it can be formulated as a multidimensional assignment problem, and …

Multi-Revolution Low-Thrust Trajectory Optimization With Very Sparse Mesh Pseudospectral Method
Yilin Zou, Fanghua Jiang
https://arxiv.org/abs/2507.01450 h…

Multi-Revolution Low-Thrust Trajectory Optimization With Very Sparse Mesh Pseudospectral Method
Multi-revolution low-thrust trajectory optimization problems are important and challenging in space mission design. In this paper, an efficient, accurate, and widely applicable pseudospectral method is proposed to solve multi-revolution low-thrust trajectory optimization problems with various objective functions and perturbations. The method is based on the Sundman transformation and pseudospectral method, together with a sparse mesh that is monotonic, near-uniformly spaced, and uniformly scatt…

A Comparative Study of Optimal Control and Neural Networks in Asteroid Rendezvous Mission Analysis
Zhong Zhang, Niccol\`o Michelotti, Gon\c{c}alo Oliveira Pinho, Francesco Topputo
https://arxiv.org/abs/2508.02920

A Comparative Study of Optimal Control and Neural Networks in Asteroid Rendezvous Mission Analysis
This paper presents a comparative study of the applicability and accuracy of optimal control methods and neural network-based estimators in the context of porkchop plots for preliminary asteroid rendezvous mission design. The scenario considered involves a deep-space CubeSat equipped with a low-thrust engine, departing from Earth and rendezvousing with a near-Earth asteroid within a three-year launch window. A low-thrust trajectory optimization model is formulated, incorporating variable specif…

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose
Yang Gao, Saeed Saadatnejad, Alexandre Alahi
https://arxiv.org/abs/2507.22742 https://arx…

Social-Pose: Enhancing Trajectory Prediction with Human Body Pose
Accurate human trajectory prediction is one of the most crucial tasks for autonomous driving, ensuring its safety. Yet, existing models often fail to fully leverage the visual cues that humans subconsciously communicate when navigating the space. In this work, we study the benefits of predicting human trajectories using human body poses instead of solely their Cartesian space locations in time. We propose `Social-pose', an attention-based pose encoder that effectively captures the poses of all …

Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN
Miroslav Cibula, Krist\'ina Malinovsk\'a, Matthias Kerzel
https://arxiv.org/abs/2507.02171

Towards Bio-Inspired Robotic Trajectory Planning via Self-Supervised RNN
Trajectory planning in robotics is understood as generating a sequence of joint configurations that will lead a robotic agent, or its manipulator, from an initial state to the desired final state, thus completing a manipulation task while considering constraints like robot kinematics and the environment. Typically, this is achieved via sampling-based planners, which are computationally intensive. Recent advances demonstrate that trajectory planning can also be performed by supervised sequence l…

This https://arxiv.org/abs/2506.00541 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

3D Trajectory Reconstruction of Moving Points Based on Asynchronous Cameras
Photomechanics is a crucial branch of solid mechanics. The localization of point targets constitutes a fundamental problem in optical experimental mechanics, with extensive applications in various missions of UAVs. Localizing moving targets is crucial for analyzing their motion characteristics and dynamic properties. Reconstructing the trajectories of points from asynchronous cameras is a significant challenge. It encompasses two coupled sub-problems: trajectory reconstruction and camera synchr…

Predictive Risk Analysis and Safe Trajectory Planning for Intelligent and Connected Vehicles
Zeyu Han, Mengchi Cai, Chaoyi Chen, Qingwen Meng, Guangwei Wang, Ying Liu, Qing Xu, Jianqiang Wang, Keqiang Li
https://arxiv.org/abs/2506.23999

Predictive Risk Analysis and Safe Trajectory Planning for Intelligent and Connected Vehicles
The safe trajectory planning of intelligent and connected vehicles is a key component in autonomous driving technology. Modeling the environment risk information by field is a promising and effective approach for safe trajectory planning. However, existing risk assessment theories only analyze the risk by current information, ignoring future prediction. This paper proposes a predictive risk analysis and safe trajectory planning framework for intelligent and connected vehicles. This framework fi…

Achieving Precise and Reliable Locomotion with Differentiable Simulation-Based System Identification
Vyacheslav Kovalev, Ekaterina Chaikovskaia, Egor Davydenko, Roman Gorbachev
https://arxiv.org/abs/2508.04696

Achieving Precise and Reliable Locomotion with Differentiable Simulation-Based System Identification
Accurate system identification is crucial for reducing trajectory drift in bipedal locomotion, particularly in reinforcement learning and model-based control. In this paper, we present a novel control framework that integrates system identification into the reinforcement learning training loop using differentiable simulation. Unlike traditional approaches that rely on direct torque measurements, our method estimates system parameters using only trajectory data (positions, velocities) and contro…

Replaced article(s) found for eess.SY. https://arxiv.org/list/eess.SY/new
[1/2]:
- Coherent Track Before Detect: Detection via simultaneous trajectory estimation and long time inte...
Kimin Kim, Murat Uney, Bernard Mulgrew

Trajectory Optimization for Differential Drive Mobile Manipulators via Topological Paths Search and Arc Length-Yaw Parameterization
Long Xu, Choilam Wong, Mengke Zhang, Junxiao Lin, Fei Gao
https://arxiv.org/abs/2507.02761

Trajectory Optimization for Differential Drive Mobile Manipulators via Topological Paths Search and Arc Length-Yaw Parameterization
We present an efficient hierarchical motion planning pipeline for differential drive mobile manipulators. Our approach first searches for multiple collisionfree and topologically distinct paths for the mobile base to extract the space in which optimal solutions may exist. Further sampling and optimization are then conducted in parallel to explore feasible whole-body trajectories. For trajectory optimization, we employ polynomial trajectories and arc length-yaw parameterization, enabling efficie…

This https://arxiv.org/abs/2505.07802 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Improving Trajectory Stitching with Flow Models
Generative models have shown great promise as trajectory planners, given their affinity to modeling complex distributions and guidable inference process. Previous works have successfully applied these in the context of robotic manipulation but perform poorly when the required solution does not exist as a complete trajectory within the training set. We identify that this is a result of being unable to plan via stitching, and subsequently address the architectural and dataset choices needed to re…

Determination of Bandwidth of Q-filter in Disturbance Observers to Guarantee Transient and Steady State Performance under Measurement Noise
Gaeun Kim, Hyungbo Shim
https://arxiv.org/abs/2507.02981

Determination of Bandwidth of Q-filter in Disturbance Observers to Guarantee Transient and Steady State Performance under Measurement Noise
Q-filter-based disturbance observer (DOB) is one of the most widely used robust controller due to its design simplicity. Such simplicity arises from that reducing the time constant of low pass filters, not only ensures robust stability but also enhances nominal performance recovery -- ability to recover the trajectory of nominal closed-loop system. However, in contrast to noise-free environment, excessively small time constant can rather damage the nominal performance recovery under measurement…

Data-Driven Stochastic Control via Non-i.i.d. Trajectories: Foundations and Guarantees
Abolfazl Lavaei
https://arxiv.org/abs/2507.23280 https://arxiv.org/p…

Data-Driven Stochastic Control via Non-i.i.d. Trajectories: Foundations and Guarantees
This work establishes a crucial step toward advancing data-driven trajectory-based methods for stochastic systems with unknown mathematical dynamics. In contrast to scenario-based approaches that rely on independent and identically distributed (i.i.d.) trajectories, this work develops a data-driven framework where each trajectory is gathered over a finite horizon and exhibits temporal dependence-referred to as a non-i.i.d. trajectory. To ensure safety of dynamical systems using such trajectorie…

Decision Transformer-Based Drone Trajectory Planning with Dynamic Safety-Efficiency Trade-Offs
Chang-Hun Ji, SiWoon Song, Youn-Hee Han, SungTae Moon
https://arxiv.org/abs/2507.21506

Decision Transformer-Based Drone Trajectory Planning with Dynamic Safety-Efficiency Trade-Offs
A drone trajectory planner should be able to dynamically adjust the safety-efficiency trade-off according to varying mission requirements in unknown environments. Although traditional polynomial-based planners offer computational efficiency and smooth trajectory generation, they require expert knowledge to tune multiple parameters to adjust this trade-off. Moreover, even with careful tuning, the resulting adjustment may fail to achieve the desired trade-off. Similarly, although reinforcement le…

Economic Model Predictive Control with a Non-Fixed Reference Trajectory for Optimal Microgrid Dispatch
Avik Ghosh, Adil Khurram, Jan Kleissl, Sonia Martinez
https://arxiv.org/abs/2506.22406

Economic Model Predictive Control with a Non-Fixed Reference Trajectory for Optimal Microgrid Dispatch
Economic Model Predictive Control (EMPC), instead of stabilizing a reference trajectory/state in the objective function like a Tracking MPC, optimizes the economic performance over the prediction horizon, making it attractive for economical microgrid (MG) dispatch. However, the demand charge component in the monthly electricity cost, make it difficult to be encapsulated in additive stage costs, and can make solutions violate the principle of optimality if naively introduced in the objective fun…

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

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

Information-Theoretic Graph Fusion with Vision-Language-Action Model for Policy Reasoning and Dual Robotic Control
Shunlei Li, Longsen Gao, Jin Wang, Chang Che, Xi Xiao, Jiuwen Cao, Yingbai Hu, Hamid Reza Karimi
https://arxiv.org/abs/2508.05342

Information-Theoretic Graph Fusion with Vision-Language-Action Model for Policy Reasoning and Dual Robotic Control
Teaching robots dexterous skills from human videos remains challenging due to the reliance on low-level trajectory imitation, which fails to generalize across object types, spatial layouts, and manipulator configurations. We propose Graph-Fused Vision-Language-Action (GF-VLA), a framework that enables dual-arm robotic systems to perform task-level reasoning and execution directly from RGB and Depth human demonstrations. GF-VLA first extracts Shannon-information-based cues to identify hands and …

A Bi-Level Optimization Method for Redundant Dual-Arm Minimum Time Problems
Jonathan Fried, Santiago Paternain
https://arxiv.org/abs/2506.03982 https://

A Bi-Level Optimization Method for Redundant Dual-Arm Minimum Time Problems
In this work, we present a method for minimizing the time required for a redundant dual-arm robot to follow a desired relative Cartesian path at constant path speed by optimizing its joint trajectories, subject to position, velocity, and acceleration limits. The problem is reformulated as a bi-level optimization whose lower level is a convex, closed-form subproblem that maximizes path speed for a fixed trajectory, while the upper level updates the trajectory using a single-chain kinematic formu…

MOSU: Autonomous Long-range Robot Navigation with Multi-modal Scene Understanding
Jing Liang, Kasun Weerakoon, Daeun Song, Senthurbavan Kirubaharan, Xuesu Xiao, Dinesh Manocha
https://arxiv.org/abs/2507.04686

MOSU: Autonomous Long-range Robot Navigation with Multi-modal Scene Understanding
We present MOSU, a novel autonomous long-range navigation system that enhances global navigation for mobile robots through multimodal perception and on-road scene understanding. MOSU addresses the outdoor robot navigation challenge by integrating geometric, semantic, and contextual information to ensure comprehensive scene understanding. The system combines GPS and QGIS map-based routing for high-level global path planning and multi-modal trajectory generation for local navigation refinement. F…

Safety Evaluation of Motion Plans Using Trajectory Predictors as Forward Reachable Set Estimators
Kaustav Chakraborty, Zeyuan Feng, Sushant Veer, Apoorva Sharma, Wenhao Ding, Sever Topan, Boris Ivanovic, Marco Pavone, Somil Bansal
https://arxiv.org/abs/2507.22389

Safety Evaluation of Motion Plans Using Trajectory Predictors as Forward Reachable Set Estimators
The advent of end-to-end autonomy stacks - often lacking interpretable intermediate modules - has placed an increased burden on ensuring that the final output, i.e., the motion plan, is safe in order to validate the safety of the entire stack. This requires a safety monitor that is both complete (able to detect all unsafe plans) and sound (does not flag safe plans). In this work, we propose a principled safety monitor that leverages modern multi-modal trajectory predictors to approximate forwar…

UniConFlow: A Unified Constrained Generalization Framework for Certified Motion Planning with Flow Matching Models
Zewen Yang, Xiaobing Dai, Dian Yu, Qianru Li, Yu Li, Valentin Le Mesle
https://arxiv.org/abs/2506.02955

UniConFlow: A Unified Constrained Generalization Framework for Certified Motion Planning with Flow Matching Models
Generative models have become increasingly powerful tools for robot motion generation, enabling flexible and multimodal trajectory generation across various tasks. Yet, most existing approaches remain limited in handling multiple types of constraints, such as collision avoidance and dynamic consistency, which are often treated separately or only partially considered. This paper proposes UniConFlow, a unified flow matching (FM) based framework for trajectory generation that systematically incorp…

This https://arxiv.org/abs/2409.01198 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Direct Kinematics, Inverse Kinematics, and Motion Planning of 1-DoF Rational Linkages
This study presents a set of algorithms that deal with trajectory planning of rational single-loop mechanisms with one degree of freedom (DoF). Benefiting from a dual quaternion representation of a rational motion, a formula for direct (forward) kinematics, a numerical inverse kinematics algorithm, and the generation of a driving-joint trajectory are provided. A novel approach using the Gauss-Newton search for the one-parameter inverse kinematics problem is presented. Additionally, a method for…

Constraint-Preserving Data Generation for Visuomotor Policy Learning
Kevin Lin, Varun Ragunath, Andrew McAlinden, Aaditya Prasad, Jimmy Wu, Yuke Zhu, Jeannette Bohg
https://arxiv.org/abs/2508.03944

Constraint-Preserving Data Generation for Visuomotor Policy Learning
Large-scale demonstration data has powered key breakthroughs in robot manipulation, but collecting that data remains costly and time-consuming. We present Constraint-Preserving Data Generation (CP-Gen), a method that uses a single expert trajectory to generate robot demonstrations containing novel object geometries and poses. These generated demonstrations are used to train closed-loop visuomotor policies that transfer zero-shot to the real world and generalize across variations in object geome…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[2/2]:
- VOTE: Vision-Language-Action Optimization with Trajectory Ensemble Voting
Lin, Taherin, Akbari, Akbari, Lu, Chen, Padir, Yang, Chen, Li, Lin, Kaeli, Zhao, Wang

Optimization of sliding control parameters for a 3-dof robot arm using genetic algorithm (GA)
Vu Ngoc Son, Pham Van Cuong, Dao Thi My Linh, Le Tieu Nien
https://arxiv.org/abs/2508.04009

Optimization of sliding control parameters for a 3-dof robot arm using genetic algorithm (GA)
This paper presents a method for optimizing the sliding mode control (SMC) parameter for a robot manipulator applying a genetic algorithm (GA). The objective of the SMC is to achieve precise and consistent tracking of the trajectory of the robot manipulator under uncertain and disturbed conditions. However, the system effectiveness and robustness depend on the choice of the SMC parameters, which is a difficult and crucial task. To solve this problem, a genetic algorithm is used to locate the op…

Incorporating Stochastic Models of Controller Behavior into Kinodynamic Efficiently Adaptive State Lattices for Mobile Robot Motion Planning in Off-Road Environments
Eric R. Damm, Eli S. Lancaster, Felix A. Sanchez, Kiana Bronder, Jason M. Gregory, Thomas M. Howard
https://arxiv.org/abs/2508.04384 …

Incorporating Stochastic Models of Controller Behavior into Kinodynamic Efficiently Adaptive State Lattices for Mobile Robot Motion Planning in Off-Road Environments
Mobile robot motion planners rely on theoretical models to predict how the robot will move through the world. However, when deployed on a physical robot, these models are subject to errors due to real-world physics and uncertainty in how the lower-level controller follows the planned trajectory. In this work, we address this problem by presenting three methods of incorporating stochastic controller behavior into the recombinant search space of the Kinodynamic Efficiently Adaptive State Lattice …

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

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

Active Disturbance Rejection Control for Trajectory Tracking of a Seagoing USV: Design, Simulation, and Field Experiments
Jelmer van der Saag, Elia Trevisan, Wouter Falkena, Javier Alonso-Mora
https://arxiv.org/abs/2506.21265

Active Disturbance Rejection Control for Trajectory Tracking of a Seagoing USV: Design, Simulation, and Field Experiments
Unmanned Surface Vessels (USVs) face significant control challenges due to uncertain environmental disturbances like waves and currents. This paper proposes a trajectory tracking controller based on Active Disturbance Rejection Control (ADRC) implemented on the DUS V2500. A custom simulation incorporating realistic waves and current disturbances is developed to validate the controller's performance, supported by further validation through field tests in the harbour of Scheveningen, the Netherla…

Improved particle swarm optimization algorithm: multi-target trajectory optimization for swarm drones
Minze Li, Wei Zhao, Ran Chen, Mingqiang Wei
https://arxiv.org/abs/2507.13647 …

Improved particle swarm optimization algorithm: multi-target trajectory optimization for swarm drones
Real-time trajectory planning for unmanned aerial vehicles (UAVs) in dynamic environments remains a key challenge due to high computational demands and the need for fast, adaptive responses. Traditional Particle Swarm Optimization (PSO) methods, while effective for offline planning, often struggle with premature convergence and latency in real-time scenarios. To overcome these limitations, we propose PE-PSO, an enhanced PSO-based online trajectory planner. The method introduces a persistent exp…

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

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

This https://arxiv.org/abs/2505.03448 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

AquaticVision: Benchmarking Visual SLAM in Underwater Environment with Events and Frames
Many underwater applications, such as offshore asset inspections, rely on visual inspection and detailed 3D reconstruction. Recent advancements in underwater visual SLAM systems for aquatic environments have garnered significant attention in marine robotics research. However, existing underwater visual SLAM datasets often lack groundtruth trajectory data, making it difficult to objectively compare the performance of different SLAM algorithms based solely on qualitative results or COLMAP reconst…

Vision-based Perception System for Automated Delivery Robot-Pedestrians Interactions
Ergi Tushe, Bilal Farooq
https://arxiv.org/abs/2508.03541 https://arxi…

Vision-based Perception System for Automated Delivery Robot-Pedestrians Interactions
The integration of Automated Delivery Robots (ADRs) into pedestrian-heavy urban spaces introduces unique challenges in terms of safe, efficient, and socially acceptable navigation. We develop the complete pipeline for a single vision sensor based multi-pedestrian detection and tracking, pose estimation, and monocular depth perception. Leveraging the real-world MOT17 dataset sequences, this study demonstrates how integrating human-pose estimation and depth cues enhances pedestrian trajectory pre…

Sequence Modeling for Time-Optimal Quadrotor Trajectory Optimization with Sampling-based Robustness Analysis
Katherine Mao, Hongzhan Yu, Ruipeng Zhang, Igor Spasojevic, M Ani Hsieh, Sicun Gao, Vijay Kumar
https://arxiv.org/abs/2506.13915

Sequence Modeling for Time-Optimal Quadrotor Trajectory Optimization with Sampling-based Robustness Analysis
Time-optimal trajectories drive quadrotors to their dynamic limits, but computing such trajectories involves solving non-convex problems via iterative nonlinear optimization, making them prohibitively costly for real-time applications. In this work, we investigate learning-based models that imitate a model-based time-optimal trajectory planner to accelerate trajectory generation. Given a dataset of collision-free geometric paths, we show that modeling architectures can effectively learn the pat…

Framework for Robust Motion Planning of Tethered Multi-Robot Systems in Marine Environments
Markus Buchholz, Ignacio Carlucho, Zebin Huang, Michele Grimaldi, Pierre Nicolay, Sumer Tuncay, Yvan R. Petillot
https://arxiv.org/abs/2508.02287

Framework for Robust Motion Planning of Tethered Multi-Robot Systems in Marine Environments
This paper introduces CoralGuide, a novel framework designed for path planning and trajectory optimization for tethered multi-robot systems. We focus on marine robotics, which commonly have tethered configurations of an Autonomous Surface Vehicle (ASV) and an Autonomous Underwater Vehicle (AUV). CoralGuide provides safe navigation in marine environments by enhancing the A* algorithm with specialized heuristics tailored for tethered ASV-AUV systems. Our method integrates catenary curve modelling…