Tootfinder

Zero-shot 3D-Aware Trajectory-Guided image-to-video generation via Test-Time Training
Ruicheng Zhang, Jun Zhou, Zunnan Xu, Zihao Liu, Jiehui Huang, Mingyang Zhang, Yu Sun, Xiu Li
https://arxiv.org/abs/2509.06723

Zero-shot 3D-Aware Trajectory-Guided image-to-video generation via Test-Time Training
Trajectory-Guided image-to-video (I2V) generation aims to synthesize videos that adhere to user-specified motion instructions. Existing methods typically rely on computationally expensive fine-tuning on scarce annotated datasets. Although some zero-shot methods attempt to trajectory control in the latent space, they may yield unrealistic motion by neglecting 3D perspective and creating a misalignment between the manipulated latents and the network's noise predictions. To address these challenge…

ZLATTE: A Geometry-Aware, Learning-Free Framework for Language-Driven Trajectory Reshaping in Human-Robot Interaction
Junhui Huang, Yuhe Gong, Changsheng Li, Xingguang Duan, Luis Figueredo
https://arxiv.org/abs/2509.06031

ZLATTE: A Geometry-Aware, Learning-Free Framework for Language-Driven Trajectory Reshaping in Human-Robot Interaction
We present ZLATTE, a geometry-aware, learning-free framework for language-driven trajectory reshaping in human-robot interaction. Unlike prior learning-based methods, ZLATTE leverages Vision-Language Models to register objects as geometric primitives and employs a Large Language Model to translate natural language instructions into explicit geometric and kinematic constraints. These constraints are integrated into a potential field optimization to adapt initial trajectories while preserving fea…

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
Luke McLennan, Yi Wang, Ryan Farell, Minh Nguyen, Chandrajit Bajaj
https://arxiv.org/abs/2509.07280

Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data
We introduce a robust framework for learning various generalized Hamiltonian dynamics from noisy, sparse phase-space data and in an unsupervised manner based on variational Bayesian inference. Although conservative, dissipative, and port-Hamiltonian systems might share the same initial total energy of a closed system, it is challenging for a single Hamiltonian network model to capture the distinctive and varying motion dynamics and physics of a phase space, from sampled observational phase spac…

Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models
Yinjie Wang, Ling Yang, Bowen Li, Ye Tian, Ke Shen, Mengdi Wang
https://arxiv.org/abs/2509.06949

Revolutionizing Reinforcement Learning Framework for Diffusion Large Language Models
We propose TraceRL, a trajectory-aware reinforcement learning framework for diffusion language models (DLMs) that incorporates preferred inference trajectory into post-training, and is applicable across different architectures. Equipped with a diffusion-based value model that enhances training stability, we demonstrate improved reasoning performance on complex math and coding tasks. Besides, it can also be applied to adapt block-specific models to larger blocks, which improves sampling flexibil…

"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…

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…

Random Trajectory Models for Complex Phenomena
Jeffrey D. Picka
https://arxiv.org/abs/2509.06963 https://arxiv.org/pdf/2509.06963

Random Trajectory Models for Complex Phenomena
Many models for complex phenomena use a model for strongly-interacting elements on a small scale to generate larger-scale simulations of some aspects of experimental realizations. These models may be agent-based (as in the case of discrete element method models for granular flow) or based on pattern-forming systems of PDEs (as in models for Raleigh-Bènard convection patterns). Often these models are purely deterministic, producing a single simulation for each set of initial conditions. If obse…

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…

Ultra-relativistic journeys through compact astrophysical objects
Shahar Hod
https://arxiv.org/abs/2509.07427 https://arxiv.org/pdf/2509.07427

Ultra-relativistic journeys through compact astrophysical objects
It has recently been proved that, for constant density stars, there is a critical value $Λ^{*}=1$ for the dimensionless density parameter $Λ\equiv 4πR^2ρ_{\text{max}}$ of the star above which the asymptotically measured travel time $T_{\text{s}}$ along a semi-circular trajectory that connects two antipodal points on the surface of the star is {\it shorter} than the travel time $T_{\text{c}}$ along the (shorter) straight-line trajectory that connects the two antipodal points through the cent…

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

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

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/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…

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 …

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

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…

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial
Bisheng Wei, Ruihong Jiang, Ruichen Zhang, Yinqiu Liu, Dusit Niyato, Yaohua Sun, Yang Lu, Yonghui Li, Shiwen Mao, Chau Yuen, Marco Di Renzo, Mugen Peng
https://arxiv.org/abs/2509.05946

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial
The rapid advancement toward sixth-generation (6G) wireless networks has significantly intensified the complexity and scale of optimization problems, including resource allocation and trajectory design, often formulated as combinatorial problems in large discrete decision spaces. However, traditional optimization methods, such as heuristics and deep reinforcement learning (DRL), struggle to meet the demanding requirements of real-time adaptability, scalability, and dynamic handling of user inte…

3I/ATLAS: In Search of the Witnesses to Its Voyage
X. P\'erez-Couto, S. Torres, E. Villaver, A. J. Mustill, M. Manteiga
https://arxiv.org/abs/2509.07678 https://

3I/ATLAS: In Search of the Witnesses to Its Voyage
3I/ATLAS is the third interstellar object discovered to date, following 1I/'Oumuamua and 2I/Borisov. Its unusually high excess velocity and active cometary nature make it a key probe of the Galactic population of icy planetesimals. Understanding its origin requires tracing its past trajectory through the Galaxy and assessing the possible role of stellar encounters, both as a potential origin and a perturber to its orbit. We integrated the orbit of 3I/ATLAS backward in time for 10 Myr, together …

Optimal Average Disk-Inspection via Fermat's Principle
Konstantinos Georgiou
https://arxiv.org/abs/2509.06334 https://arxiv.org/pdf/2509.06334

Optimal Average Disk-Inspection via Fermat's Principle
This work resolves the optimal average-case cost of the Disk-Inspection problem, a variant of Bellman's 1955 lost-in-a-forest problem. In Disk-Inspection, a mobile agent starts at the center of a unit disk and follows a trajectory that inspects perimeter points whenever the disk does not obstruct visibility. The worst-case cost was solved optimally in 1957 by Isbell, but the average-case version remained open, with heuristic upper bounds proposed by Gluss in 1961 and improved only recently. O…

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. …

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…

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…

Recursive Hierarchical Networks and the Law of Functional Evolution: A Universal Framework for Complex Systems
Hui Li, Yanxin Li
https://arxiv.org/abs/2509.05567 https://…

Recursive Hierarchical Networks and the Law of Functional Evolution: A Universal Framework for Complex Systems
Understanding and predicting the evolution of across complex systems remains a fundamental challenge due to the absence of unified and computationally testable frameworks. Here we propose the Recursive Hierarchical Network(RHN), conceptualizing evolution as recursive encapsulation along a trajectory of node $\to$ module $\to$ system $\to$ new node, governed by gradual accumulation and abrupt transition. Theoretically, we formalize and prove the law of functional evolution, revealing an irrevers…

Metamorphic Testing of Multimodal Human Trajectory Prediction
Helge Spieker, Nadjib Lazaar, Arnaud Gotlieb, Nassim Belmecheri
https://arxiv.org/abs/2509.01294 https://

Metamorphic Testing of Multimodal Human Trajectory Prediction
Context: Predicting human trajectories is crucial for the safety and reliability of autonomous systems, such as automated vehicles and mobile robots. However, rigorously testing the underlying multimodal Human Trajectory Prediction (HTP) models, which typically use multiple input sources (e.g., trajectory history and environment maps) and produce stochastic outputs (multiple possible future paths), presents significant challenges. The primary difficulty lies in the absence of a definitive test …

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…

Hybrid Reinforcement Learning and Search for Flight Trajectory Planning
Alberto Luise, Michele Lombardi, Florent Teichteil Koenigsbuch
https://arxiv.org/abs/2509.04100 https://

Hybrid Reinforcement Learning and Search for Flight Trajectory Planning
This paper explores the combination of Reinforcement Learning (RL) and search-based path planners to speed up the optimization of flight paths for airliners, where in case of emergency a fast route re-calculation can be crucial. The fundamental idea is to train an RL Agent to pre-compute near-optimal paths based on location and atmospheric data and use those at runtime to constrain the underlying path planning solver and find a solution within a certain distance from the initial guess. The appr…

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…

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…

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…

"""
Customarily, the honour of having liberated hysteria from the ancient myths about a displacement of the uterus goes to Le Pois and Willis. Jean Liebaud, translating or rather adapting Marinello’s work for the seventeenth century, still accepted (with a small number of caveats) the idea of a spontaneous movement of the womb. If it moved, it was “to be more at ease; not that this came about through prudence, nor was it a conscious decision or an animal stimulus, but by a natural instinct, to safeguard health and to have the pleasure of something delectable.” The idea that it could change its place and move around the body, bringing convulsions and spasms everywhere it travelled, had been abandoned, for it was now taken to be ‘tightly held in place’ by the cervix, ligaments, vessels and the sheath of the peritoneum; yet in some senses it could change its location. “The womb therefore, even though it is tightly fixed to the parts that we have described and cannot easily change its place, still manages to roam, making strange, petulant movements around the woman’s body. These diverse movements include ascensions and descents, convulsions, wanderings and prolapses. It can wander up to the liver, spleen, diaphragm, stomach, chest, heart, lung, throat and head.” Physicians of the classical age are more or less unanimous in refusing this explanation.
[…] Yet these analyses were not sufficient to break the theme of an essential link between hysteria and the womb. But the link is now conceived in different terms. It is no longer considered to be the trajectory of a real displacement through the body, but rather a sort of mute propagation through the paths of the organism and its functional proximities. It cannot be said that the seat of the malady has become the brain, nor that thanks to Willis a psychological explanation of hysteria was now possible. But the brain does take on the role of a relay that distributes a malady whose origins are visceral, and the womb brings it on just as the other viscera do. Up until the end of the eighteenth century, and Pinel, the uterus and the womb are still present in the pathology of hysteria, but thanks to a privileged diffusion by the humours and nerves, not because of any particular prestige of their nature.
"""
(Michel Foucault, History of Madness)

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…

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…

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…

A Latent Class Bayesian Model for Multivariate Longitudinal Outcomes with Excess Zeros
Chitradipa Chakraborty, Kiranmoy Das
https://arxiv.org/abs/2509.04804 https://

A Latent Class Bayesian Model for Multivariate Longitudinal Outcomes with Excess Zeros
Latent class models have been successfully used to handle complex datasets in different disciplines. For longitudinal outcomes, we often get a trajectory of the outcome for each individual, and on that basis, we cluster them for a powerful statistical inference. Latent class models have been used to handle multivariate longitudinal outcomes coming from biology, health sciences, and economics. In this paper, we propose a Bayesian latent class model for multivariate outcomes with excess zeros. We…

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

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

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 …

A Million-Point Fast Trajectory Optimization Solver
A. Javeed, D. P. Kouri, D. Ridzal, J. D. Steinman, I. M. Ross
https://arxiv.org/abs/2509.01855 https://…

A Million-Point Fast Trajectory Optimization Solver
One might argue that solving a trajectory optimization problem over a million grid points is preposterous. How about solving such a problem at an incredibly fast computational time? On a small form-factor processor? Algorithmic details that make possible this trifecta of breakthroughs are presented in this paper. The computational mathematics that deliver these advancements are: (i) a Birkhoff-theoretic discretization of optimal control problems, (ii) matrix-free linear algebra leveraging Krylo…

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…

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…

BiND: A Neural Discriminator-Decoder for Accurate Bimanual Trajectory Prediction in Brain-Computer Interfaces
Timothee Robert, MohammadAli Shaeri, Mahsa Shoaran
https://arxiv.org/abs/2509.03521

BiND: A Neural Discriminator-Decoder for Accurate Bimanual Trajectory Prediction in Brain-Computer Interfaces
Decoding bimanual hand movements from intracortical recordings remains a critical challenge for brain-computer interfaces (BCIs), due to overlapping neural representations and nonlinear interlimb interactions. We introduce BiND (Bimanual Neural Discriminator-Decoder), a two-stage model that first classifies motion type (unimanual left, unimanual right, or bimanual) and then uses specialized GRU-based decoders, augmented with a trial-relative time index, to predict continuous 2D hand velocities.…

Traj-MLLM: Can Multimodal Large Language Models Reform Trajectory Data Mining?
Shuo Liu, Di Yao, Yan Lin, Gao Cong, Jingping Bi
https://arxiv.org/abs/2509.00053 https://

Traj-MLLM: Can Multimodal Large Language Models Reform Trajectory Data Mining?
Building a general model capable of analyzing human trajectories across different geographic regions and different tasks becomes an emergent yet important problem for various applications. However, existing works suffer from the generalization problem, \ie, they are either restricted to train for specific regions or only suitable for a few tasks. Given the recent advances of multimodal large language models (MLLMs), we raise the question: can MLLMs reform current trajectory data mining and solv…

Unified trajectory criterion for quantum and classical non-Markovianity
Le Hu, Archana Kamal
https://arxiv.org/abs/2508.16064 https://arxiv.org/pdf/2508.16…

Unified trajectory criterion for quantum and classical non-Markovianity
We propose a simple criterion for non-Markovianity: a quantum master equation is non-Markovian if and only if its \textit{trajectory set} contains a \textit{self-intersecting trajectory} (defined in the main text). Since self-intersection is invariant under time reversal, our criterion implies that Markovianity itself is time-reversal invariant: This property is not possessed by many existing criteria based on information flow or complete positivity. For a given quantum master equation, the set…

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…

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 …

Entry and penetration of a superhydrophobic sphere into a deep pool
Prasanna Kumar Billa, Cameron Tropea, Pallab Sinha Mahapatra
https://arxiv.org/abs/2509.03928 https://…

Entry and penetration of a superhydrophobic sphere into a deep pool
This study experimentally examines the entry and penetration of a superhydrophobic sphere into a quiescent deep pool, with special emphasis placed on the primary and secondary pinch-off of the air cavity existing in its wake. Two aspects are novel in this study. For one, the experiments are performed for a large range of dimensionless sphere densities, where lighter spheres, with their air cavity, exhibit a terminally ascending trajectory and heavier spheres a terminally descending trajectory. …

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…

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…

Double Ionization to CO2 Produces Molecular Oxygen: A Roaming Mechanism
Qibo Ma, Xintai Hao, Jiaqi Zhou, Xiaorui Xue, Qingrui Zeng, Peng Li, Lei Wang, Xueguang Ren
https://arxiv.org/abs/2509.05626

Double Ionization to CO2 Produces Molecular Oxygen: A Roaming Mechanism
We report a combined experimental and theoretical study on the formation of O2+ by electronimpact double ionization and fragmentation of carbon dioxide (CO2) molecule. Through fragment ions and electron coincidence momentum imaging, we determine unambiguously the ionization mechanism by measuring the projectile energy loss in association with the C+ + O2+ channel. Further potential energy and trajectory calculations enable us to elucidate the dynamical details of this fragmentation process, in …

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 …

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

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…

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…

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…

Trajectory learning for ensemble forecasts via the continuous ranked probability score: a Lorenz '96 case study
Sagy Ephrati, James Woodfield
https://arxiv.org/abs/2508.21664

Trajectory learning for ensemble forecasts via the continuous ranked probability score: a Lorenz '96 case study
This paper demonstrates the feasibility of trajectory learning for ensemble forecasts by employing the continuous ranked probability score (CRPS) as a loss function. Using the two-scale Lorenz '96 system as a case study, we develop and train both additive and multiplicative stochastic parametrizations to generate ensemble predictions. Results indicate that CRPS-based trajectory learning produces parametrizations that are both accurate and sharp. The resulting parametrizations are straightforwar…

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…

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…

Sensitivity-Driven Adaptive Surrogate Modeling for Simulation and Optimization of Dynamical Systems
Jonathan R. Cangelosi, Matthias Heinkenschloss
https://arxiv.org/abs/2509.04651

Sensitivity-Driven Adaptive Surrogate Modeling for Simulation and Optimization of Dynamical Systems
This paper develops a surrogate model refinement approach for the simulation of dynamical systems and the solution of optimization problems governed by dynamical systems in which surrogates replace expensive-to-compute state- and control-dependent component functions in the dynamics or objective function. For example, trajectory simulation and optimization tasks for an aircraft depend on aerodynamic coefficient functions whose evaluation requires expensive computational fluid dynamics simulatio…

"""
In the sixteenth century, lunacy was a constant theme that was never questioned. It was still frequent in the seventeenth century, but started to disappear, and by 1707, the year in which Le François asked the question ‘Estne aliquod lunae in corpora humana imperium?’ (Does the moon have any influence over the human body?), after lengthy discussions, the university decided that their reply was in the negative. In the course of the eighteenth century the moon was rarely cited among the causes of madness, even as a possible factor or an aggravation. But right at the end of the century the idea reappears, perhaps under the influence of English medicine, which had never entirely forgotten the moon, and Daquin, followed by Leuret and Guislain, all admitted the influence of the moon on the phases of maniacal excitement, or at the least on the agitation of their patients. But what is important here is not so much the return of the theme as the possibility and conditions necessary for its reappearance. It reappears entirely transformed, filled with a new significance that it did not formerly possess. In its traditional form, it designated an immediate influence, a direct coincidence in time and intersection in space, whose mode of action was entirely situated in the power of the stars. But in Daquin by contrast, the influence of the moon acts through a whole series of mediations, in a kind of hierarchy, surrounding man. The moon acts on the atmosphere with such intensity that it can set in motion a mass as heavy as the ocean. The nervous system, of all the parts that make up the human organism, is the part most sensitive to atmospheric variations, as the slightest variation in temperature, humidity or dryness can have serious effects upon it. The moon therefore, given the important power that its trajectory exerts on the atmosphere, is likely to act most on people whose nervous fibres are particularly delicate:
“Madness is an exclusively nervous condition, and the brain of a madman must therefore be infinitely more susceptible to the influence of the atmosphere, which itself undergoes considerable changes of intensity as a result of the different positions of the moon relative to the earth.” [Daquin, Philosophie de la folie, Paris, 1792]
"""
(Michel Foucault, History of Madness)

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…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- BiND: A Neural Discriminator-Decoder for Accurate Bimanual Trajectory Prediction in Brain-Compute...
Timothee Robert, MohammadAli Shaeri, Mahsa Shoaran

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…

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…

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…

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…

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…

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…

Globally Asymptotically Stable Trajectory Tracking of Underactuated UAVs using Geometric Algebra
Ignacio Rubio Scola, Omar Alejandro Garcia Alcantara, Steven Sandoval, Eduardo Steed Espinoza Quesada, Hernan Haimovich, Luis Rodolfo Garcia Carrillo
https://arxiv.org/abs/2509.03484

Globally Asymptotically Stable Trajectory Tracking of Underactuated UAVs using Geometric Algebra
This paper employs Geometric Algebra (GA) tools to model the dynamics of objects in 3-dimensional space, serving as a proof of concept to facilitate control design for trajectory tracking in underactuated systems. For control purposes, the model is structured as a cascade system, where a rotational subsystem drives a translational one. The rotational subsystem is linear, while the translational subsystem follows a linear-plus-perturbation form, thereby reducing the complexity of control design.…

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

Multi-vessel Interaction-Aware Trajectory Prediction and Collision Risk Assessment
Md Mahbub Alam, Jose F. Rodrigues-Jr, Gabriel Spadon
https://arxiv.org/abs/2509.01836 https://…

Multi-vessel Interaction-Aware Trajectory Prediction and Collision Risk Assessment
Accurate vessel trajectory prediction is essential for enhancing situational awareness and preventing collisions. Still, existing data-driven models are constrained mainly to single-vessel forecasting, overlooking vessel interactions, navigation rules, and explicit collision risk assessment. We present a transformer-based framework for multi-vessel trajectory prediction with integrated collision risk analysis. For a given target vessel, the framework identifies nearby vessels. It jointly predic…

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 …

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…

High-Performance Trajectory Tracking MPC for Quadcopters with Coupled Time-Varying Constraints and Stability Proofs
Maedeh Izadi, A. T. J. R. Cobbenhagen, R. L. Sommer, A. R. P. Andrien, E. Lefeber, W. P. M. H. Heemels
https://arxiv.org/abs/2509.01767

High-Performance Trajectory Tracking MPC for Quadcopters with Coupled Time-Varying Constraints and Stability Proofs
In this paper, we present a cascade control structure to address the trajectory tracking problem for quadcopters, ensuring uniform global asymptotic stability of the state tracking error dynamics. An MPC strategy based on a 12-dimensional prediction model is proposed for the outer loop, explicitly accounting for time-varying coupled constraints, where multiple variables are interdependent and need to be handled together. The outer-loop controller generates an acceleration reference, which is th…

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…

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…

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 …

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[2/2]:
- Learning to Coordinate: Distributed Meta-Trajectory Optimization Via Differentiable ADMM-DDP
Bingheng Wang, Yichao Gao, Tianchen Sun, Lin Zhao

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…

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…

TrajSV: A Trajectory-based Model for Sports Video Representations and Applications
Zheng Wang, Shihao Xu, Wei Shi
https://arxiv.org/abs/2508.11569 https://…

TrajSV: A Trajectory-based Model for Sports Video Representations and Applications
Sports analytics has received significant attention from both academia and industry in recent years. Despite the growing interest and efforts in this field, several issues remain unresolved, including (1) data unavailability, (2) lack of an effective trajectory-based framework, and (3) requirement for sufficient supervision labels. In this paper, we present TrajSV, a trajectory-based framework that addresses various issues in existing studies. TrajSV comprises three components: data preprocessi…

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…

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…

Toward a Holistic Multi-Criteria Trajectory Evaluation Framework for Autonomous Driving in Mixed Traffic Environment
Nouhed Naidja (L2S, VeDeCom), St\'ephane Font (L2S), Marc Revilloud (L2S), Guillaume Sandou (L2S)
https://arxiv.org/abs/2509.01291

Toward a Holistic Multi-Criteria Trajectory Evaluation Framework for Autonomous Driving in Mixed Traffic Environment
This paper presents a unified framework for the evaluation and optimization of autonomous vehicle trajectories, integrating formal safety, comfort, and efficiency criteria. An innovative geometric indicator, based on the analysis of safety zones using adaptive ellipses, is used to accurately quantify collision risks. Our method applies the Shoelace formula to compute the intersection area in the case of misaligned and time-varying configurations. Comfort is modeled using indicators centered on …

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…

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…

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…

Dynamics-Compliant Trajectory Diffusion for Super-Nominal Payload Manipulation
Anuj Pasricha, Joewie Koh, Jay Vakil, Alessandro Roncone
https://arxiv.org/abs/2508.21375 https://…

Dynamics-Compliant Trajectory Diffusion for Super-Nominal Payload Manipulation
Nominal payload ratings for articulated robots are typically derived from worst-case configurations, resulting in uniform payload constraints across the entire workspace. This conservative approach severely underutilizes the robot's inherent capabilities -- our analysis demonstrates that manipulators can safely handle payloads well above nominal capacity across broad regions of their workspace while staying within joint angle, velocity, acceleration, and torque limits. To address this gap betwe…

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…

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…

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

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

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…

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…