Tootfinder

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…

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 …

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…

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…

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…

From Seeing to Predicting: A Vision-Language Framework for Trajectory Forecasting and Controlled Video Generation
Fan Yang, Zhiyang Chen, Yousong Zhu, Xin Li, Jinqiao Wang
https://arxiv.org/abs/2510.00806

From Seeing to Predicting: A Vision-Language Framework for Trajectory Forecasting and Controlled Video Generation
Current video generation models produce physically inconsistent motion that violates real-world dynamics. We propose TrajVLM-Gen, a two-stage framework for physics-aware image-to-video generation. First, we employ a Vision Language Model to predict coarse-grained motion trajectories that maintain consistency with real-world physics. Second, these trajectories guide video generation through attention-based mechanisms for fine-grained motion refinement. We build a trajectory prediction dataset ba…

Closed-form Single UAV-aided Emitter Localization and Trajectory Design Using Doppler and TOA Measurements
Samaneh Motie, Hadi Zayyani, Mohammad Salman, Hasan Abu Hilal
https://arxiv.org/abs/2510.01778

Closed-form Single UAV-aided Emitter Localization and Trajectory Design Using Doppler and TOA Measurements
In this paper, a single Unmanned-Aerial-Vehicle (UAV)-aided localization algorithm which uses both Doppler and Time of Arrival (ToA) measurements is presented. In contrast to Doppler-based localization algorithms which are based on non-convex functions, exploiting ToA measurements in a Least-Square (LS) Doppler-based cost function, leads to a quadratic convex function whose minimizer lies on a line. Utilizing the ToA measurements in addition to the linear equation of minimizer, a closed form so…

The Trajectory Bundle Method: Unifying Sequential-Convex Programming and Sampling-Based Trajectory Optimization
Kevin Tracy, John Z. Zhang, Jon Arrizabalaga, Stefan Schaal, Yuval Tassa, Tom Erez, Zachary Manchester
https://arxiv.org/abs/2509.26575

The Trajectory Bundle Method: Unifying Sequential-Convex Programming and Sampling-Based Trajectory Optimization
We present a unified framework for solving trajectory optimization problems in a derivative-free manner through the use of sequential convex programming. Traditionally, nonconvex optimization problems are solved by forming and solving a sequence of convex optimization problems, where the cost and constraint functions are approximated locally through Taylor series expansions. This presents a challenge for functions where differentiation is expensive or unavailable. In this work, we present a der…

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.…

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 …

Crosslisted article(s) found for cs.CL. https://arxiv.org/list/cs.CL/new
[1/3]:
- Traj-MLLM: Can Multimodal Large Language Models Reform Trajectory Data Mining?
Shuo Liu, Di Yao, Yan Lin, Gao Cong, Jingping Bi

Measurement-induced phase transition in interacting bosons from most likely quantum trajectory
Anna Delmonte, Zejian Li, Rosario Fazio, Alessandro Romito
https://arxiv.org/abs/2509.24520

Measurement-induced phase transition in interacting bosons from most likely quantum trajectory
We propose a new theoretical method to describe the monitored dynamics of bosonic many-body systems based on the concept of the most likely trajectory. We show how such trajectory can be identified from the probability distribution of quantum trajectories, i.e. measurement readouts, and how it successfully captures the monitored dynamics beyond the average state. We prove the method to be exact in the case of Gaussian theories and then extend it to the interacting Sine-Gordon model. Although no…

Efficient Hyperparameter Tuning via Trajectory Invariance Principle
Bingrui Li, Jiaxin Wen, Zhanpeng Zhou, Jun Zhu, Jianfei Chen
https://arxiv.org/abs/2509.25049 https://…

Efficient Hyperparameter Tuning via Trajectory Invariance Principle
As hyperparameter tuning becomes increasingly costly at scale, efficient tuning methods are essential. Yet principles for guiding hyperparameter tuning remain limited. In this work, we seek to establish such principles by considering a broad range of hyperparameters, including batch size, learning rate, and weight decay. We identify a phenomenon we call trajectory invariance, where pre-training loss curves, gradient noise, and gradient norm exhibit invariance--closely overlapping--with respect …

Barack Obama headlined a rally Saturday in Virginia
to try to secure a victory for the state’s Democratic gubernatorial candidate, who leads in polls days before the election.
Obama moved between criticizing Donald Trump and Republican policy and rhetoric – with a bit of humor
– while also explaining how Abigail Spanberger could help counter what Democrats see as the country’s downward trajectory.
“As for the president, he has been focused on critical issues like pavi…

Nonlinear Continuum of States and Intuitionistic Flows in a Cognitive Space
Faruk Alpay, Taylan Alpay
https://arxiv.org/abs/2509.02613 https://arxiv.org/pd…

Nonlinear Continuum of States and Intuitionistic Flows in a Cognitive Space
We construct a rigorous mathematical framework for an abstract continuous evolution of an internal state, inspired by the intuitive notion of a flowing thought sequence. Using tools from topology, functional analysis, measure theory, and logic, we formalize an indefinitely proceeding sequence of states as a non-linear continuum with rich structure. In our development, a trajectory of states is modeled as a continuous mapping on a topological state space (a potentially infinite-dimensional Banac…

Thermality and athermality in the Unruh effect
D. Jaffino Stargen, Vivishek Sudhir
https://arxiv.org/abs/2509.01983 https://arxiv.org/pdf/2509.01983…

Thermality and athermality in the Unruh effect
In idealized treatments of the Unruh effect, a two-level atom is accelerated in a prescribed classical trajectory through the vacuum of a quantum field -- the Unruh bath -- which causes the atom's internal state to thermalize to a temperature proportional to the acceleration. This happens via emission and absorption of quanta by the atom, leading to a detailed balance between fluctuations in the Unruh bath and associated dissipation of the atom's internal energy. In any physical manifestation o…

A variational formulation of stochastic thermodynamics. Part I: Finite-dimensional systems
H\'ector Vaquero del Pino, Fran\c{c}ois Gay-Balmaz, Hiroaki Yoshimura, Lock Yue Chew
https://arxiv.org/abs/2510.01787

A variational formulation of stochastic thermodynamics. Part I: Finite-dimensional systems
In this paper, we develop a variational foundation for stochastic thermodynamics of finite-dimensional, continuous-time systems. Requiring the second law (non-negative average total entropy production) systematically yields a consistent thermodynamic structure, from which novel generalized fluctuation-dissipation relations emerge naturally, ensuring local detailed balance. This principle extends key results of stochastic thermodynamics including an individual trajectory level description of bot…

Crosslisted article(s) found for cs.MS. https://arxiv.org/list/cs.MS/new
[1/1]:
- A Million-Point Fast Trajectory Optimization Solver
A. Javeed, D. P. Kouri, D. Ridzal, J. D. Steinman, I. M. Ross

Noise estimation of SDE from a single data trajectory
Munawar Ali, Purba Das, Qi Feng, Liyao Gao, Guang Lin
https://arxiv.org/abs/2509.25484 https://arxiv.…

Noise estimation of SDE from a single data trajectory
In this paper, we propose a data-driven framework for model discovery of stochastic differential equations (SDEs) from a single trajectory, without requiring the ergodicity or stationary assumption on the underlying continuous process. By combining (stochastic) Taylor expansions with Girsanov transformations, and using the drift function's initial value as input, we construct drift estimators while simultaneously recovering the model noise. This allows us to recover the underlying $\mathbb P$ B…

Remember when? Deciphering Ediacaran-Cambrian Metazoan behaviour and temporal memory using fossil movement paths
Brittany A. Laing, M. Gabriela M\'angano, Luis A. Buatois, Glenn A. Brock, Romain Gougeon, Zoe Vestrum, Luke C. Strotz, Lyndon Koens
https://arxiv.org/abs/2509.02940

Remember when? Deciphering Ediacaran-Cambrian Metazoan behaviour and temporal memory using fossil movement paths
Evaluating the timing and trajectory of sensory system innovations is crucial for understanding the increase in phylogenetic, behavioural, and ecological diversity during the Ediacaran-Cambrian transition. Elucidation of sensory adaptations has relied on either body-fossil evidence based on anatomical features or qualitative descriptions of trace-fossil morphology, leaving a gap in the record of sensory system innovations between the development of basic sensory capacities and that of more adva…

An Observations-focused Assessment of Global AI Weather Prediction Models During the South Asian Monsoon
Aman Gupta, Aditi Sheshadri, Dhruv Suri
https://arxiv.org/abs/2509.01879

An Observations-focused Assessment of Global AI Weather Prediction Models During the South Asian Monsoon
Seven state-of-the-art AI weather models (FourCastNet, FourCastNet-SFNO, Pangu-Weather, GraphCast, Aurora, AIFS, and GenCast) are evaluated against observational data during the South Asian Monsoon. The models are tested on temperature, winds, global kinetic energy spectrum, regional precipitation, cloud cover, cyclone trajectory prediction, and hyperlocal predictions around extreme weather events. The models forecast large-scale dynamics with reasonable accuracy, but fall short on key metrics …

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…

Robust Data-Driven Control for Nonlinear Systems Using their Digital Twins and Quadratic Funnels
Shiva Shakeri, Mehran Mesbahi
https://arxiv.org/abs/2510.01406 https://

Robust Data-Driven Control for Nonlinear Systems Using their Digital Twins and Quadratic Funnels
This paper examines a robust data-driven approach for the safe deployment of systems with nonlinear dynamics using their imperfect digital twins. Our contribution involves proposing a method that fuses the digital twin's nominal trajectory with online, data-driven uncertainty quantification to synthesize robust tracking controllers. Specifically, we derive data-driven bounds to capture the deviations of the actual system from its prescribed nominal trajectory informed via its digital twin. Subs…

Enhancing Connectivity for Emergency Vehicles Through UAV Trajectory and Resource Allocation Optimization
S. Fatemeh Bozorgi, S. Mohammad Razavizadeh, Mohsen Rezaee
https://arxiv.org/abs/2509.26067

Enhancing Connectivity for Emergency Vehicles Through UAV Trajectory and Resource Allocation Optimization
Effective communication for emergency vehicles - such as ambulances and fire trucks - is essential to support their operations in various traffic and environmental conditions. In this context, this paper investigates a vehicular communication system assisted by an Unmanned Aerial Vehicle (UAV), which adjusts its trajectory and resource allocation according to communication needs. The system classifies vehicles into two groups to address their varying service requirements: emergency vehicles, wh…

Snakelike trajectories of electrons released from quantum dots driven by the spin Hall effect
B. Szafran, P. Wojcik
https://arxiv.org/abs/2509.24824 https://

Snakelike trajectories of electrons released from quantum dots driven by the spin Hall effect
Using time dependent simulations, we analyze the trajectories of electrons released from a quantum dot in a waveguide made of a spin-orbit-coupled material (InSb). An electron released from the quantum dot, when driven by an electric field follows a trajectory that is deflected by spin-orbit interaction and undergoes spin precession that results in a spin-dependent, snake-like trajectory. The trajectory strongly depends on the initial state of the electron, enabling detection of the electron qu…

Strong gravitational lensing by compact objects with magnetic dipole
Jin Young Kim
https://arxiv.org/abs/2509.01040 https://arxiv.org/pdf/2509.01040…

Strong gravitational lensing by compact objects with magnetic dipole
Compact objects with magnetic dipole are considered as gravitational lenses. The presence of strong magnetic field near the photon sphere can affect the trajectory of light. We compute the deflection angle near the photon sphere on the equatorial plane of the magnetic dipole. In the Einstein-Maxwell gravity we use the asymptotic metric when a massive object has a magnetic dipole moment. In the Einstein-Born-Infeld gravity we use the effective metric accomodating the nonlinear electrodynamic eff…

CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
Kangyu Wu, Jiaqi Qiao, Ya Zhang
https://arxiv.org/abs/2509.15984

CoPAD : Multi-source Trajectory Fusion and Cooperative Trajectory Prediction with Anchor-oriented Decoder in V2X Scenarios
Recently, data-driven trajectory prediction methods have achieved remarkable results, significantly advancing the development of autonomous driving. However, the instability of single-vehicle perception introduces certain limitations to trajectory prediction. In this paper, a novel lightweight framework for cooperative trajectory prediction, CoPAD, is proposed. This framework incorporates a fusion module based on the Hungarian algorithm and Kalman filtering, along with the Past Time Attention (…

Text2Move: Text-to-moving sound generation via trajectory prediction and temporal alignment
Yunyi Liu, Shaofan Yang, Kai Li, Xu Li
https://arxiv.org/abs/2509.21919 https://

Text2Move: Text-to-moving sound generation via trajectory prediction and temporal alignment
Human auditory perception is shaped by moving sound sources in 3D space, yet prior work in generative sound modelling has largely been restricted to mono signals or static spatial audio. In this work, we introduce a framework for generating moving sounds given text prompts in a controllable fashion. To enable training, we construct a synthetic dataset that records moving sounds in binaural format, their spatial trajectories, and text captions about the sound event and spatial motion. Using this…

TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion
Sophia Tang, Yuchen Zhu, Molei Tao, Pranam Chatterjee
https://arxiv.org/abs/2509.25171 https://

TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion
Reinforcement learning with stochastic optimal control offers a promising framework for diffusion fine-tuning, where a pre-trained diffusion model is optimized to generate paths that lead to a reward-tilted distribution. While these approaches enable optimization without access to explicit samples from the optimal distribution, they require training on rollouts under the current fine-tuned model, making them susceptible to reinforcing sub-optimal trajectories that yield poor rewards. To overcom…

Fault-tolerant Model Predictive Control for Spacecraft
Raphael St\"ockner, Pedro Roque, Maria Charitidou, Dimos V. Dimarogonas
https://arxiv.org/abs/2509.02527 https://

Fault-tolerant Model Predictive Control for Spacecraft
Given the cost and critical functions of satellite constellations, ensuring mission longevity and safe decommissioning is essential for space sustainability. This article presents a Model Predictive Control for spacecraft trajectory and setpoint stabilization under multiple actuation failures. The proposed solution allows us to efficiently control the faulty spacecraft enabling safe navigation towards servicing or collision-free trajectories. The proposed scheme ensures closed-loop asymptotic s…

A Measurement Study of Model Context Protocol
Hechuan Guo, Yongle Hao, Yue Zhang, Minghui Xu, Peizhuo Lyu, Jiezhi Chen, Xiuzhen Cheng
https://arxiv.org/abs/2509.25292 https://…

A Measurement Study of Model Context Protocol
The Model Context Protocol (MCP) has been proposed as a unifying standard for connecting large language models (LLMs) with external tools and resources, promising the same role for AI integration that HTTP and USB played for the Web and peripherals. Yet, despite rapid adoption and hype, its trajectory remains uncertain. Are MCP marketplaces truly growing, or merely inflated by placeholders and abandoned prototypes? Are servers secure and privacy-preserving, or do they expose users to systemic r…

Off-Trajectory Reasoning: Can LLMs Collaborate on Reasoning Trajectory?
Aochong Oliver Li, Tanya Goyal
https://arxiv.org/abs/2510.06410 https://arxiv.org/p…

Off-Trajectory Reasoning: Can LLMs Collaborate on Reasoning Trajectory?
Reasoning LLMs are trained to verbalize their reasoning process, yielding strong gains on complex tasks. This transparency also opens a promising direction: multiple reasoners can directly collaborate on each other's thinking within a shared trajectory, yielding better inference efficiency and exploration. A key prerequisite, however, is the ability to assess the usefulness and build on another model's partial thinking -- we call this off-trajectory reasoning. Our paper investigates a critical …

Agentic AI Empowered Multi-UAV Trajectory Optimization in Low-Altitude Economy Networks
Feibo Jiang, Li Dong, Xitao Pan, Kezhi Wang, Cunhua Pan
https://arxiv.org/abs/2508.16379 …

Agentic AI Empowered Multi-UAV Trajectory Optimization in Low-Altitude Economy Networks
This paper proposes a novel Agentic Retrieval-augmented generation with Mamba-Attention Integrated Transformer (ARMAIT) framework for multi-Unmanned Aerial Vehicle (UAV) trajectory optimization. The framework is built upon Large Language Models (LLMs), incorporating Retrieval-Augmented Generation (RAG) empowered by Agentic AI and integrated with a UAV-specific knowledge base. Through the Agentic RAG, the LLM autonomously interprets high-level task requirements and identifies the key components …

Holography vs. Scale Separation
Alek Bedroya, Paul J. Steinhardt
https://arxiv.org/abs/2509.25313 https://arxiv.org/pdf/2509.25313

Holography vs. Scale Separation
In this work, we point out a contradiction between holography and scale-separated AdS (i.e. parametrically large mass gap) in string theory, making the standard assumption that the holographic CFT describes the IR degrees of freedom on a brane that decouple from gravity. We show that the CFT can only decouple from gravity if the scalar potential in the dual AdS satisfies a certain criterion. Namely, there must exist a scalar field trajectory that follows the gradient of the scalar potential to …

Super-resolution reconstruction of turbulent flows from a single Lagrangian trajectory
Hua-Lin Wu, Ao Xu, Heng-Dong Xi
https://arxiv.org/abs/2509.17109 https://

Super-resolution reconstruction of turbulent flows from a single Lagrangian trajectory
We studied the reconstruction of turbulent flow fields from trajectory data recorded by actively migrating Lagrangian agents. We propose a deep learning model, Track-to-Flow (T2F), which employs a Vision Transformer as an encoder to capture the spatiotemporal features of a single agent trajectory, and a convolutional neural network as the decoder to reconstruct the flow field. To enhance the physical consistency of the T2F model, we further incorporate a physics-informed loss function inspired …

A Spectral-Grassmann Wasserstein metric for operator representations of dynamical systems
Thibaut Germain, R\'emi Flamary, Vladimir R. Kostic, Karim Lounici
https://arxiv.org/abs/2509.24920

A Spectral-Grassmann Wasserstein metric for operator representations of dynamical systems
The geometry of dynamical systems estimated from trajectory data is a major challenge for machine learning applications. Koopman and transfer operators provide a linear representation of nonlinear dynamics through their spectral decomposition, offering a natural framework for comparison. We propose a novel approach representing each system as a distribution of its joint operator eigenvalues and spectral projectors and defining a metric between systems leveraging optimal transport. The proposed …

Like Playing a Video Game: Spatial-Temporal Optimization of Foot Trajectories for Controlled Football Kicking in Bipedal Robots
Wanyue Li, Ji Ma, Minghao Lu, Peng Lu
https://arxiv.org/abs/2510.01843

Like Playing a Video Game: Spatial-Temporal Optimization of Foot Trajectories for Controlled Football Kicking in Bipedal Robots
Humanoid robot soccer presents several challenges, particularly in maintaining system stability during aggressive kicking motions while achieving precise ball trajectory control. Current solutions, whether traditional position-based control methods or reinforcement learning (RL) approaches, exhibit significant limitations. Model predictive control (MPC) is a prevalent approach for ordinary quadruped and biped robots. While MPC has demonstrated advantages in legged robots, existing studies often…

Upcoming Vandenberg SFB
rocket launch schedule,
featuring today's and upcoming launches.
Our page offers detailed insights into each mission, including trajectory, crew details, mission objectives, rocket specifications, launch location, and booster landing updates.
Watch live Vandenberg SFB video feeds and stay informed about the latest developments.

Vandenberg SFB Rocket Launch Schedule: Today & Upcoming - Space Launch Schedule
Vandenberg SFB Rocket Launch Schedule: Live Video Feed, Trajectory, Location, Booster Landing Information Today & Upcoming - Space Launch Schedule

Undecidability of Finite Orbit Recognition in Polynomial Maps
Gwangyong Gwon
https://arxiv.org/abs/2510.00412 https://arxiv.org/pdf/2510.00412

Undecidability of Finite Orbit Recognition in Polynomial Maps
We prove the undecidability of determining whether a Turing machine yields an eventually periodic trajectory. From this, we deduce the undecidability of orbit finiteness in the polynomial dynamical system on infinite tuples of integers.

Generalized Fermat's principle and Snell's law for cone structures and applications
Miguel \'Angel Javaloyes, Steen Markvorsen, Enrique Pend\'as-Recondo, Miguel S\'anchez
https://arxiv.org/abs/2509.26492

Generalized Fermat's principle and Snell's law for cone structures and applications
Fermat's principle is fully generalized to the case where a smooth interface separates two cone structures -- Lorentz-Finsler lightcones -- representing wave propagation in a potentially inhomogeneous, anisotropic, time-dependent and discontinuous medium. The interface, wave source and receiver are assumed to be a hypersurface, a submanifold and a curve in the spacetime, respectively, of any causal character. For a trajectory to fullfil Fermat's principle -- i.e., to be a critical point of the …

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…

Preference Trajectory Modeling via Flow Matching for Sequential Recommendation
Li Li, Mingyue Cheng, Yuyang Ye, Zhiding Liu, Enhong Chen
https://arxiv.org/abs/2508.17618 https:/…

Preference Trajectory Modeling via Flow Matching for Sequential Recommendation
Sequential recommendation predicts each user's next item based on their historical interaction sequence. Recently, diffusion models have attracted significant attention in this area due to their strong ability to model user interest distributions. They typically generate target items by denoising Gaussian noise conditioned on historical interactions. However, these models face two critical limitations. First, they exhibit high sensitivity to the condition, making it difficult to recover target …

An Efficient Shift-and-Stack Algorithm Applied to Detection Catalogs
Steven Stetzler, Mario Juri\'c, Pedro H. Bernardinelli, Dino Bekte\v{s}evi\'c, Colin Orion Chandler, Andrew J. Connolly, Fred C. Adams, Cesar Fuentes, David W. Gerdes, Matthew J. Holman, Hsing Wen Lin, Larissa Markwardt, Andrew McNeill, Michael Mommert, Kevin J. Napier, William J. Oldroyd, Matthew J. Payne, Andrew S. Rivkin, Luis E. Salazar-Manzano, Hilke Schlichting, Scott S. Sheppard, Dallin Spencer, Ryder S…

An Efficient Shift-and-Stack Algorithm Applied to Detection Catalogs
The boundary of solar system object discovery lies in detecting its faintest members. However, their discovery in detection catalogs from imaging surveys is fundamentally limited by the practice of thresholding detections at signal-to-noise (SNR) $\geq 5$ to maintain catalog purity. Faint moving objects can be recovered from survey images using the shift-and-stack algorithm, which coadds pixels from multi-epoch images along a candidate trajectory. Trajectories matching real objects accumulate s…

Towards Data-Driven Metrics for Social Robot Navigation Benchmarking
Pilar Bachiller-Burgos, Ulysses Bernardet, Luis V. Calderita, Pranup Chhetri, Anthony Francis, Noriaki Hirose, No\'e P\'erez, Dhruv Shah, Phani T. Singamaneni, Xuesu Xiao, Luis J. Manso
https://arxiv.org/abs/2509.01251

Towards Data-Driven Metrics for Social Robot Navigation Benchmarking
This paper presents a joint effort towards the development of a data-driven Social Robot Navigation metric to facilitate benchmarking and policy optimization. We provide our motivations for our approach and describe our proposal for storing rated social navigation trajectory datasets. Following these guidelines, we compiled a dataset with 4427 trajectories -- 182 real and 4245 simulated -- and presented it to human raters, yielding a total of 4402 rated trajectories after data quality assurance…

Hidden Convexity in Active Learning: A Convexified Online Input Design for ARX Systems
Nicolas Chatzikiriakos, Bowen Song, Philipp Rank, Andrea Iannelli
https://arxiv.org/abs/2509.03257

Hidden Convexity in Active Learning: A Convexified Online Input Design for ARX Systems
The goal of this work is to accelerate the identification of an unknown ARX system from trajectory data through online input design. Specifically, we present an active learning algorithm that sequentially selects the input to excite the system according to an experiment design criterion using the past measured data. The adopted criterion yields a non-convex optimization problem, but we provide an exact convex reformulation allowing to find the global optimizer in a computationally tractable way…

JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation
Guillem Capellera, Luis Ferraz, Antonio Rubio, Alexandre Alahi, Antonio Agudo
https://arxiv.org/abs/2509.22522

JointDiff: Bridging Continuous and Discrete in Multi-Agent Trajectory Generation
Generative models often treat continuous data and discrete events as separate processes, creating a gap in modeling complex systems where they interact synchronously. To bridge this gap, we introduce JointDiff, a novel diffusion framework designed to unify these two processes by simultaneously generating continuous spatio-temporal data and synchronous discrete events. We demonstrate its efficacy in the sports domain by simultaneously modeling multi-agent trajectories and key possession events. …

Differentiable Skill Optimisation for Powder Manipulation in Laboratory Automation
Minglun Wei, Xintong Yang, Yu-Kun Lai, Ze Ji
https://arxiv.org/abs/2510.01438 https://

Differentiable Skill Optimisation for Powder Manipulation in Laboratory Automation
Robotic automation is accelerating scientific discovery by reducing manual effort in laboratory workflows. However, precise manipulation of powders remains challenging, particularly in tasks such as transport that demand accuracy and stability. We propose a trajectory optimisation framework for powder transport in laboratory settings, which integrates differentiable physics simulation for accurate modelling of granular dynamics, low-dimensional skill-space parameterisation to reduce optimisatio…

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

Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems
Margarita A. Guerrero, Braghadeesh Lakshminarayanan, Cristian R. Rojas
https://arxiv.org/abs/2509.26334

Gain-Scheduled Data-Enabled Predictive Control: A DeePC Approach for Nonlinear Systems
Model predictive control is a well established control technology for trajectory tracking. Its use requires the availability of an accurate model of the plant, but obtaining such a model is often time consuming and costly. Data-Enabled Predictive Control (DeePC) addresses this shortcoming in the linear time-invariant setting, by skipping the model building step and instead relying directly on input-output data. Unfortunately, many real systems are nonlinear and exhibit strong operating-point de…

AdaSports-Traj: Role- and Domain-Aware Adaptation for Multi-Agent Trajectory Modeling in Sports
Yi Xu, Yun Fu
https://arxiv.org/abs/2509.16095 https://arxi…

AdaSports-Traj: Role- and Domain-Aware Adaptation for Multi-Agent Trajectory Modeling in Sports
Trajectory prediction in multi-agent sports scenarios is inherently challenging due to the structural heterogeneity across agent roles (e.g., players vs. ball) and dynamic distribution gaps across different sports domains. Existing unified frameworks often fail to capture these structured distributional shifts, resulting in suboptimal generalization across roles and domains. We propose AdaSports-Traj, an adaptive trajectory modeling framework that explicitly addresses both intra-domain and inte…

A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
Ziteng Gao, Jiaqi Qu, Chaoyu Chen
https://arxiv.org/abs/2509.01611 https:/…

A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle
Lane change decision-making for autonomous vehicles is a complex but high-reward behavior. In this paper, we propose a hybrid input based deep reinforcement learning (DRL) algorithm, which realizes abstract lane change decisions and lane change actions for autonomous vehicles within traffic flow. Firstly, a surrounding vehicles trajectory prediction method is proposed to reduce the risk of future behavior of surrounding vehicles to ego vehicle, and the prediction results are input into the rein…

Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes
Guangwen Li, Xiao Yu
https://arxiv.org/abs/2509.25580 https://arxiv.org/pdf…

Neural-Model-Augmented Hybrid NMS-OSD Decoders for Near-ML in Short Block Codes
This paper introduces a hybrid decoding architecture that serially couples a normalized min-sum (NMS) decoder with reinforced ordered statistics decoding (OSD) to achieve near-maximum likelihood (ML) performance for short linear block codes. The framework incorporates several key innovations: a decoding information aggregation model that employs a convolutional neural network to refine bit reliability estimates for OSD using the soft-output trajectory of the NMS decoder; an adaptive decoding pa…

Task and Motion Planning of Dynamic Systems using Hyperproperties for Signal Temporal Logics
Jianing Zhao, Bowen Ye, Xinyi Yu, Rupak Majumdar, Xiang Yin
https://arxiv.org/abs/2509.02184

Task and Motion Planning of Dynamic Systems using Hyperproperties for Signal Temporal Logics
We investigate the task and motion planning problem for dynamical systems under signal temporal logic (STL) specifications. Existing works on STL control synthesis mainly focus on generating plans that satisfy properties over a single executed trajectory. In this work, we consider the planning problem for hyperproperties evaluated over a set of possible trajectories, which naturally arise in information-flow control problems. Specifically, we study discrete-time dynamical systems and employ the…

SupCLAP: Controlling Optimization Trajectory Drift in Audio-Text Contrastive Learning with Support Vector Regularization
Jiehui Luo, Yuguo Yin, Yuxin Xie, Jinghan Ru, Xianwei Zhuang, Minghua He, Aofan Liu, Zihan Xiong, Dongchao Yang
https://arxiv.org/abs/2509.21033

SupCLAP: Controlling Optimization Trajectory Drift in Audio-Text Contrastive Learning with Support Vector Regularization
Contrastive language-audio pretraining, which aims to unify multimodal representations in a shared embedding space, serves as a cornerstone for building a wide range of applications, from cross-modal retrieval to cutting-edge multimodal large language models. However, we find that the perpendicular component of the pushing force from negative samples in contrastive learning is a double-edged sword: it contains rich supplementary information from negative samples, yet its unconstrained nature ca…

ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration
Gaole Dai, Shiqi Jiang, Ting Cao, Yuqing Yang, Yuanchun Li, Rui Tan, Mo Li, Lili Qiu
https://arxiv.org/abs/2509.21823

ProRe: A Proactive Reward System for GUI Agents via Reasoner-Actor Collaboration
Reward is critical to the evaluation and training of large language models (LLMs). However, existing rule-based or model-based reward methods struggle to generalize to GUI agents, where access to ground-truth trajectories or application databases is often unavailable, and static trajectory-based LLM-as-a-Judge approaches suffer from limited accuracy. To address these challenges, we propose ProRe, a proactive reward system that leverages a general-purpose reasoner and domain-specific evaluator a…

Trajectory Prediction via Bayesian Intention Inference under Unknown Goals and Kinematics
Shunan Yin, Zehui Lu, Shaoshuai Mou
https://arxiv.org/abs/2509.24928 https://

Trajectory Prediction via Bayesian Intention Inference under Unknown Goals and Kinematics
This work introduces an adaptive Bayesian algorithm for real-time trajectory prediction via intention inference, where a target's intentions and motion characteristics are unknown and subject to change. The method concurrently estimates two critical variables: the target's current intention, modeled as a Markovian latent state, and an intention parameter that describes the target's adherence to a shortest-path policy. By integrating this joint update technique, the algorithm maintains robustnes…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/4]:
- BiTrajDiff: Bidirectional Trajectory Generation with Diffusion Models for Offline Reinforcement L...
Yunpeng Qing, Shuo Chen, Yixiao Chi, Shunyu Liu, Sixu Lin, Kelu Yao, Changqing Zou

FlowDrive: moderated flow matching with data balancing for trajectory planning
Lingguang Wang, \"Omer \c{S}ahin Ta\c{s}, Marlon Steiner, Christoph Stiller
https://arxiv.org/abs/2509.21961

FlowDrive: moderated flow matching with data balancing for trajectory planning
Learning-based planners are sensitive to the long-tailed distribution of driving data. Common maneuvers dominate datasets, while dangerous or rare scenarios are sparse. This imbalance can bias models toward the frequent cases and degrade performance on critical scenarios. To tackle this problem, we compare balancing strategies for sampling training data and find reweighting by trajectory pattern an effective approach. We then present FlowDrive, a flow-matching trajectory planner that learns a c…

Quantum Reinforcement Learning with Dynamic-Circuit Qubit Reuse and Grover-Based Trajectory Optimization
Thet Htar Su, Shaswot Shresthamali, Masaaki Kondo
https://arxiv.org/abs/2509.16002

Quantum Reinforcement Learning with Dynamic-Circuit Qubit Reuse and Grover-Based Trajectory Optimization
A fully quantum reinforcement learning framework is developed that integrates a quantum Markov decision process, dynamic circuit-based qubit reuse, and Grover's algorithm for trajectory optimization. The framework encodes states, actions, rewards, and transitions entirely within the quantum domain, enabling parallel exploration of state-action sequences through superposition and eliminating classical subroutines. Dynamic circuit operations, including mid-circuit measurement and reset, allow reu…

I don’t expect the World Liberty Financial scandal,
—which The New York Times exposed in a superb investigative piece Monday
—to follow a recognizable trajectory.
That’s because nothing since January has followed a recognizable trajectory.
In the world I know, the Times story would initiate a Justice Department investigation and an impeachment inquiry,
and Trump’s Mideast envoy Steve Witkoff could be counted on to resign by week’s end.
In the mystifying w…

Does Anybody Care This Is the Worst Bribery Scandal Since Teapot Dome?
The UAE put $2 billion into World Liberty Financial, then got export restrictions lifted on AI chips. Nothing to see here, move along.

MotionFlow:Learning Implicit Motion Flow for Complex Camera Trajectory Control in Video Generation
Guojun Lei, Chi Wang, Yikai Wang, Hong Li, Ying Song, Weiwei Xu
https://arxiv.org/abs/2509.21119

MotionFlow:Learning Implicit Motion Flow for Complex Camera Trajectory Control in Video Generation
Generating videos guided by camera trajectories poses significant challenges in achieving consistency and generalizability, particularly when both camera and object motions are present. Existing approaches often attempt to learn these motions separately, which may lead to confusion regarding the relative motion between the camera and the objects. To address this challenge, we propose a novel approach that integrates both camera and object motions by converting them into the motion of correspond…

Movable-Antenna Trajectory Optimization for Wireless Sensing: CRB Scaling Laws over Time and Space
Wenyan Ma, Lipeng Zhu, Rui Zhang
https://arxiv.org/abs/2509.14905 https://

Movable-Antenna Trajectory Optimization for Wireless Sensing: CRB Scaling Laws over Time and Space
In this paper, we present a new wireless sensing system utilizing a movable antenna (MA) that continuously moves and receives sensing signals to enhance sensing performance over the conventional fixed-position antenna (FPA) sensing. We show that the angle estimation performance is fundamentally determined by the MA trajectory, and derive the Cramer-Rao bound (CRB) of the mean square error (MSE) for angle-of-arrival (AoA) estimation as a function of the trajectory for both one-dimensional (1D) a…

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…

Beyond expected value: geometric mean optimization for long-term policy performance in reinforcement learning
Xinyi Sheng, Dominik Baumann
https://arxiv.org/abs/2508.21443 https…

Beyond expected value: geometric mean optimization for long-term policy performance in reinforcement learning
Reinforcement learning (RL) algorithms typically optimize the expected cumulative reward, i.e., the expected value of the sum of scalar rewards an agent receives over the course of a trajectory. The expected value averages the performance over an infinite number of trajectories. However, when deploying the agent in the real world, this ensemble average may be uninformative for the performance of individual trajectories. Thus, in many applications, optimizing the long-term performance of individ…

Integrated Take-off Management and Trajectory Optimization for Merging Control in Urban Air Mobility Corridors
Yingqi Liu, Tianlu Pan, Jingjun Tan, Renxin Zhong, Can Chen
https://arxiv.org/abs/2508.15395

Integrated Take-off Management and Trajectory Optimization for Merging Control in Urban Air Mobility Corridors
Urban Air Mobility (UAM) has the potential to revolutionize daily transportation, offering rapid and efficient aerial mobility services. Take-off and merging phases are critical for air corridor operations, requiring the coordination of take-off aircraft and corridor traffic while ensuring safety and seamless transition. This paper proposes an integrated take-off management and trajectory optimization for merging control in UAM corridors. We first introduce a novel take-off airspace design. To …

Orcust: Stepwise-Feedback Reinforcement Learning for GUI Agent
Junyu Lu, Songxin Zhang, Zejian Xie, Zhuoyang Song, Jiaxing Zhang
https://arxiv.org/abs/2509.17917 https://…

Orcust: Stepwise-Feedback Reinforcement Learning for GUI Agent
Recent advances in GUI agents have achieved remarkable grounding and action-prediction performance, yet existing models struggle with unreliable reward signals and limited online trajectory generation. In this paper, we introduce Orcust, a framework that integrates Principle-Constrained Reward Modeling (PCRM) and Online VM-Grounded Trajectory Construction (OVTC) to enhance reasoning reliability and data efficiency in interactive GUI tasks. We leverages environment-verifiable and LLM-derived pri…

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…

Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering
Evelyn D'Elia, Paolo Maria Viceconte, Lorenzo Rapetti, Diego Ferigo, Giulio Romualdi, Giuseppe L'Erario, Raffaello Camoriano, Daniele Pucci
https://arxiv.org/abs/2509.24697

Stabilizing Humanoid Robot Trajectory Generation via Physics-Informed Learning and Control-Informed Steering
Recent trends in humanoid robot control have successfully employed imitation learning to enable the learned generation of smooth, human-like trajectories from human data. While these approaches make more realistic motions possible, they are limited by the amount of available motion data, and do not incorporate prior knowledge about the physical laws governing the system and its interactions with the environment. Thus they may violate such laws, leading to divergent trajectories and sliding cont…

ExtraGS: Geometric-Aware Trajectory Extrapolation with Uncertainty-Guided Generative Priors
Kaiyuan Tan, Yingying Shen, Haohui Zhu, Zhiwei Zhan, Shan Zhao, Mingfei Tu, Hongcheng Luo, Haiyang Sun, Bing Wang, Guang Chen, Hangjun Ye
https://arxiv.org/abs/2508.15529

ExtraGS: Geometric-Aware Trajectory Extrapolation with Uncertainty-Guided Generative Priors
Synthesizing extrapolated views from recorded driving logs is critical for simulating driving scenes for autonomous driving vehicles, yet it remains a challenging task. Recent methods leverage generative priors as pseudo ground truth, but often lead to poor geometric consistency and over-smoothed renderings. To address these limitations, we propose ExtraGS, a holistic framework for trajectory extrapolation that integrates both geometric and generative priors. At the core of ExtraGS is a novel R…

HAEPO: History-Aggregated Exploratory Policy Optimization
Gaurish Trivedi, Alakh Sharma, Kartikey Singh Bhandari, Dhruv Kumar, Pratik Narang, Jagat Sesh Challa
https://arxiv.org/abs/2508.18884

HAEPO: History-Aggregated Exploratory Policy Optimization
Exploration is essential in modern learning, from reinforcement learning environments with small neural policies to large language models (LLMs). Existing work, such as DPO, leverages full sequence log-likelihoods to capture an entire trajectory of the model's decisions, while methods like GRPO aggregate per-token ratios into a trajectory-level update. However, both often limit exploration on long-horizon tasks. We introduce History-Aggregated Exploratory Policy Optimization (HAEPO), a history-…

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

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

Trajectory Encryption Cooperative Salvo Guidance
Lohitvel Gopikannan, Shashi Ranjan Kumar, Abhinav Sinha
https://arxiv.org/abs/2509.17341 https://arxiv.org…

Trajectory Encryption Cooperative Salvo Guidance
This paper introduces the concept of trajectory encryption in cooperative simultaneous target interception, wherein heterogeneity in guidance principles across a team of unmanned autonomous systems is leveraged as a strategic design feature. By employing a mix of heterogeneous time-to-go formulations leading to a cooperative guidance strategy, the swarm of vehicles is able to generate diverse trajectory families. This diversity expands the feasible solution space for simultaneous target interce…

FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis
Xichen Xu, Yanshu Wang, Jinbao Wang, Xiaoning Lei, Guoyang Xie, Guannan Jiang, Zhichao Lu
https://arxiv.org/abs/2509.20295

FAST: Foreground-aware Diffusion with Accelerated Sampling Trajectory for Segmentation-oriented Anomaly Synthesis
Industrial anomaly segmentation relies heavily on pixel-level annotations, yet real-world anomalies are often scarce, diverse, and costly to label. Segmentation-oriented industrial anomaly synthesis (SIAS) has emerged as a promising alternative; however, existing methods struggle to balance sampling efficiency and generation quality. Moreover, most approaches treat all spatial regions uniformly, overlooking the distinct statistical differences between anomaly and background areas. This uniform …

Scaling with Collapse: Efficient and Predictable Training of LLM Families
Shane Bergsma, Bin Claire Zhang, Nolan Dey, Shaheer Muhammad, Gurpreet Gosal, Joel Hestness
https://arxiv.org/abs/2509.25087

Scaling with Collapse: Efficient and Predictable Training of LLM Families
Effective LLM training relies on *consistency*, meaning that key quantities -- such as final losses and optimal hyperparameters -- scale predictably across model sizes. Qiu et al. (2025) recently showed that this consistency extends beyond scalars: whole training loss curves can *collapse* onto a universal trajectory after a simple normalization. What remains unclear is whether this phenomenon holds for LLM families trained under *practical scaling recipes*, where width, depth, learning rate, b…

C-3TO: Continuous 3D Trajectory Optimization on Neural Euclidean Signed Distance Fields
Guillermo Gil (Service Robotics Laboratory, Universidad Pablo de Olavide, Seville, Spain), Jose Antonio Cobano (Service Robotics Laboratory, Universidad Pablo de Olavide, Seville, Spain), Luis Merino (Service Robotics Laboratory, Universidad Pablo de Olavide, Seville, Spain), Fernando Caballero (Service Robotics Laboratory, Universidad Pablo de Olavide, Seville, Spain)

C-3TO: Continuous 3D Trajectory Optimization on Neural Euclidean Signed Distance Fields
This paper introduces a novel framework for continuous 3D trajectory optimization in cluttered environments, leveraging online neural Euclidean Signed Distance Fields (ESDFs). Unlike prior approaches that rely on discretized ESDF grids with interpolation, our method directly optimizes smooth trajectories represented by fifth-order polynomials over a continuous neural ESDF, ensuring precise gradient information throughout the entire trajectory. The framework integrates a two-stage nonlinear opti…

FlexTraj: Image-to-Video Generation with Flexible Point Trajectory Control
Zhiyuan Zhang, Can Wang, Dongdong Chen, Jing Liao
https://arxiv.org/abs/2510.08527 https://

FlexTraj: Image-to-Video Generation with Flexible Point Trajectory Control
We present FlexTraj, a framework for image-to-video generation with flexible point trajectory control. FlexTraj introduces a unified point-based motion representation that encodes each point with a segmentation ID, a temporally consistent trajectory ID, and an optional color channel for appearance cues, enabling both dense and sparse trajectory control. Instead of injecting trajectory conditions into the video generator through token concatenation or ControlNet, FlexTraj employs an efficient se…

Adaptive Lyapunov-constrained MPC for fault-tolerant AUV trajectory tracking
Haolin Liu, Shiliang Zhang, Xiaohui Zhang, Shangbin Jiao, Xuehui Ma, Ting Shang, Yan Yan, Wenqi Bai, Youmin Zhang
https://arxiv.org/abs/2509.17237

Adaptive Lyapunov-constrained MPC for fault-tolerant AUV trajectory tracking
Autonomous underwater vehicles (AUVs) are subject to various sources of faults during their missions, which challenges AUV control and operation in real environments. This paper addresses fault-tolerant trajectory tracking of autonomous underwater vehicles (AUVs) under thruster failures. We propose an adaptive Lyapunov-constrained model predictive control (LMPC) that guarantees stable trajectory tracking when the AUV switches between fault and normal modes. Particularly, we model different AUV …

Trajectory Planning Using Safe Ellipsoidal Corridors as Projections of Orthogonal Trust Regions
Akshay Jaitly, Jon Arrizabalaga, Guanrui Li
https://arxiv.org/abs/2509.19734 http…

Trajectory Planning Using Safe Ellipsoidal Corridors as Projections of Orthogonal Trust Regions
Planning collision free trajectories in complex environments remains a core challenge in robotics. Existing corridor based planners which rely on decomposition of the free space into collision free subsets scale poorly with environmental complexity and require explicit allocations of time windows to trajectory segments. We introduce a new trajectory parameterization that represents trajectories in a nonconvex collision free corridor as being in a convex cartesian product of balls. This paramete…

Great GATsBi: Hybrid, Multimodal, Trajectory Forecasting for Bicycles using Anticipation Mechanism
Kevin Riehl, Shaimaa K. El-Baklish, Anastasios Kouvelas, Michail A. Makridis
https://arxiv.org/abs/2508.14523

Great GATsBi: Hybrid, Multimodal, Trajectory Forecasting for Bicycles using Anticipation Mechanism
Accurate prediction of road user movement is increasingly required by many applications ranging from advanced driver assistance systems to autonomous driving, and especially crucial for road safety. Even though most traffic accident fatalities account to bicycles, they have received little attention, as previous work focused mainly on pedestrians and motorized vehicles. In this work, we present the Great GATsBi, a domain-knowledge-based, hybrid, multimodal trajectory prediction framework for bi…

Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators
Yongliang Wang, Hamidreza Kasaei
https://arxiv.org/abs/2509.17381 https://…

Fast Trajectory Planner with a Reinforcement Learning-based Controller for Robotic Manipulators
Generating obstacle-free trajectories for robotic manipulators in unstructured and cluttered environments remains a significant challenge. Existing motion planning methods often require additional computational effort to generate the final trajectory by solving kinematic or dynamic equations. This paper highlights the strong potential of model-free reinforcement learning methods over model-based approaches for obstacle-free trajectory planning in joint space. We propose a fast trajectory planni…

OmniCache: A Trajectory-Oriented Global Perspective on Training-Free Cache Reuse for Diffusion Transformer Models
Huanpeng Chu, Wei Wu, Guanyu Fen, Yutao Zhang
https://arxiv.org/abs/2508.16212

OmniCache: A Trajectory-Oriented Global Perspective on Training-Free Cache Reuse for Diffusion Transformer Models
Diffusion models have emerged as a powerful paradigm for generative tasks such as image synthesis and video generation, with Transformer architectures further enhancing performance. However, the high computational cost of diffusion Transformers-stemming from a large number of sampling steps and complex per-step computations-presents significant challenges for real-time deployment. In this paper, we introduce OmniCache, a training-free acceleration method that exploits the global redundancy inhe…

Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
Yuting Zeng, Zhiwen Zheng, You Zhou, JiaLing Xiao, Yongbin Yu, Manping Fan, Bo Gong, Liyong Ren
https://arxiv.org/abs/2509.15582

Momentum-constrained Hybrid Heuristic Trajectory Optimization Framework with Residual-enhanced DRL for Visually Impaired Scenarios
This paper proposes a momentum-constrained hybrid heuristic trajectory optimization framework (MHHTOF) tailored for assistive navigation in visually impaired scenarios, integrating trajectory sampling generation, optimization and evaluation with residual-enhanced deep reinforcement learning (DRL). In the first stage, heuristic trajectory sampling cluster (HTSC) is generated in the Frenet coordinate system using third-order interpolation with fifth-order polynomials and momentum-constrained traj…

Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
Divya Thuremella, Yi Yang, Simon Wanna, Lars Kunze, Daniele De Martini
https://arxiv.org/abs/2509.13914 http…

Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
This work explores the application of ensemble modeling to the multidimensional regression problem of trajectory prediction for vehicles in urban environments. As newer and bigger state-of-the-art prediction models for autonomous driving continue to emerge, an important open challenge is the problem of how to combine the strengths of these big models without the need for costly re-training. We show how, perhaps surprisingly, combining state-of-the-art deep learning models out-of-the-box (withou…

High-Precision and High-Efficiency Trajectory Tracking for Excavators Based on Closed-Loop Dynamics
Ziqing Zou, Cong Wang, Yue Hu, Xiao Liu, Bowen Xu, Rong Xiong, Changjie Fan, Yingfeng Chen, Yue Wang
https://arxiv.org/abs/2509.17387

High-Precision and High-Efficiency Trajectory Tracking for Excavators Based on Closed-Loop Dynamics
The complex nonlinear dynamics of hydraulic excavators, such as time delays and control coupling, pose significant challenges to achieving high-precision trajectory tracking. Traditional control methods often fall short in such applications due to their inability to effectively handle these nonlinearities, while commonly used learning-based methods require extensive interactions with the environment, leading to inefficiency. To address these issues, we introduce EfficientTrack, a trajectory tra…

BEVTraj: Map-Free End-to-End Trajectory Prediction in Bird's-Eye View with Deformable Attention and Sparse Goal Proposals
Minsang Kong, Myeongjun Kim, Sang Gu Kang, Sang Hun Lee
https://arxiv.org/abs/2509.10080

BEVTraj: Map-Free End-to-End Trajectory Prediction in Bird's-Eye View with Deformable Attention and Sparse Goal Proposals
In autonomous driving, trajectory prediction is essential for ensuring safe and efficient navigation. To improve prediction accuracy, recent approaches often rely on pre-built high-definition (HD) maps or real-time local map construction modules to incorporate static environmental information. However, pre-built HD maps are limited to specific regions and cannot adapt to transient changes. In addition, local map construction modules, which recognize only predefined elements, may fail to capture…

AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving
Jinhao Chai, Anqing Jiang, Hao Jiang, Shiyi Mu, Zichong Gu, Shugong Xu
https://arxiv.org/abs/2509.20253

AnchDrive: Bootstrapping Diffusion Policies with Hybrid Trajectory Anchors for End-to-End Driving
End-to-end multi-modal planning has become a transformative paradigm in autonomous driving, effectively addressing behavioral multi-modality and the generalization challenge in long-tail scenarios. We propose AnchDrive, a framework for end-to-end driving that effectively bootstraps a diffusion policy to mitigate the high computational cost of traditional generative models. Rather than denoising from pure noise, AnchDrive initializes its planner with a rich set of hybrid trajectory anchors. Thes…

Assessing Human Cooperation for Enhancing Social Robot Navigation
Hariharan Arunachalam, Phani Teja Singamaneni, Rachid Alami
https://arxiv.org/abs/2508.21455 https://

Assessing Human Cooperation for Enhancing Social Robot Navigation
Socially aware robot navigation is a planning paradigm where the robot navigates in human environments and tries to adhere to social constraints while interacting with the humans in the scene. These navigation strategies were further improved using human prediction models, where the robot takes the potential future trajectory of humans while computing its own. Though these strategies significantly improve the robot's behavior, it faces difficulties from time to time when the human behaves in an…

Robust Real-Time Coordination of CAVs: A Distributed Optimization Framework under Uncertainty
Haojie Bai, Yang Wang, Cong Guo, Xiongwei Zhao, Hai Zhu
https://arxiv.org/abs/2508.21322

Robust Real-Time Coordination of CAVs: A Distributed Optimization Framework under Uncertainty
Achieving both safety guarantees and real-time performance in cooperative vehicle coordination remains a fundamental challenge, particularly in dynamic and uncertain environments. This paper presents a novel coordination framework that resolves this challenge through three key innovations: 1) direct control of vehicles' trajectory distributions during coordination, formulated as a robust cooperative planning problem with adaptive enhanced safety constraints, ensuring a specified level of safety…

SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model
Xiao Zhou, Zengqi Peng, Jun Ma
https://arxiv.org/abs/2509.17850

SocialTraj: Two-Stage Socially-Aware Trajectory Prediction for Autonomous Driving via Conditional Diffusion Model
Accurate trajectory prediction of surrounding vehicles (SVs) is crucial for autonomous driving systems to avoid misguided decisions and potential accidents. However, achieving reliable predictions in highly dynamic and complex traffic scenarios remains a significant challenge. One of the key impediments lies in the limited effectiveness of current approaches to capture the multi-modal behaviors of drivers, which leads to predicted trajectories that deviate from actual future motions. To address…

\LARGE GMP$^{3}$: Learning-Driven, Bellman-Guided Trajectory Planning for UAVs in Real-Time on SE(3)
Babak Salamat, Dominik Mattern, Sebastian-Sven Olzem, Gerhard Elsbacher, Christian Seidel, Andrea M. Tonello
https://arxiv.org/abs/2509.21264

\LARGE GMP$^{3}$: Learning-Driven, Bellman-Guided Trajectory Planning for UAVs in Real-Time on SE(3)
We propose $\text{GMP}^{3}$, a multiphase global path planning framework that generates dynamically feasible three-dimensional trajectories for unmanned aerial vehicles (UAVs) operating in cluttered environments. The framework extends traditional path planning from Euclidean position spaces to the Lie group $\mathrm{SE}(3)$, allowing joint learning of translational motion and rotational dynamics. A modified Bellman-based operator is introduced to support reinforcement learning (RL) policy updat…

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…

ReSeFlow: Rectifying SE(3)-Equivariant Policy Learning Flows
Zhitao Wang, Yanke Wang, Jiangtao Wen, Roberto Horowitz, Yuxing Han
https://arxiv.org/abs/2509.22695 https://…

ReSeFlow: Rectifying SE(3)-Equivariant Policy Learning Flows
Robotic manipulation in unstructured environments requires the generation of robust and long-horizon trajectory-level policy with conditions of perceptual observations and benefits from the advantages of SE(3)-equivariant diffusion models that are data-efficient. However, these models suffer from the inference time costs. Inspired by the inference efficiency of rectified flows, we introduce the rectification to the SE(3)-diffusion models and propose the ReSeFlow, i.e., Rectifying SE(3)-Equivari…

SafeBimanual: Diffusion-based Trajectory Optimization for Safe Bimanual Manipulation
Haoyuan Deng, Wenkai Guo, Qianzhun Wang, Zhenyu Wu, Ziwei Wang
https://arxiv.org/abs/2508.18268

SafeBimanual: Diffusion-based Trajectory Optimization for Safe Bimanual Manipulation
Bimanual manipulation has been widely applied in household services and manufacturing, which enables the complex task completion with coordination requirements. Recent diffusion-based policy learning approaches have achieved promising performance in modeling action distributions for bimanual manipulation. However, they ignored the physical safety constraints of bimanual manipulation, which leads to the dangerous behaviors with damage to robots and objects. To this end, we propose a test-time tr…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[4/4]:
- What Do You Need for Diverse Trajectory Composition in Diffusion Planning?
Quentin Clark, Florian Shkurti

Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Korbinian Moller, Roland Stroop, Mattia Piccinini, Alexander Langmann, Johannes Betz
https://arxiv.org/abs/2509.24313

Learning to Sample: Reinforcement Learning-Guided Sampling for Autonomous Vehicle Motion Planning
Sampling-based motion planning is a well-established approach in autonomous driving, valued for its modularity and analytical tractability. In complex urban scenarios, however, uniform or heuristic sampling often produces many infeasible or irrelevant trajectories. We address this limitation with a hybrid framework that learns where to sample while keeping trajectory generation and evaluation fully analytical and verifiable. A reinforcement learning (RL) agent guides the sampling process toward…

DBF-MA: A Differential Bayesian Filtering Planner for Multi-Agent Autonomous Racing Overtakes
Trent Weiss, Amar Kulkarni, Madhur Behl
https://arxiv.org/abs/2509.22937 https://…

DBF-MA: A Differential Bayesian Filtering Planner for Multi-Agent Autonomous Racing Overtakes
A significant challenge in autonomous racing is to generate overtaking maneuvers. Racing agents must execute these maneuvers on complex racetracks with little room for error. Optimization techniques and graph-based methods have been proposed, but these methods often rely on oversimplified assumptions for collision-avoidance and dynamic constraints. In this work, we present an approach to trajectory synthesis based on an extension of the Differential Bayesian Filtering framework. Our approach fo…

Trajectory Tracking and Stabilization of Quadrotors Using Deep Koopman Model Predictive Control
Haitham El-Hussieny
https://arxiv.org/abs/2508.13795 https://

Trajectory Tracking and Stabilization of Quadrotors Using Deep Koopman Model Predictive Control
This paper presents a data-driven control framework for quadrotor systems that integrates a deep Koopman operator with model predictive control (DK-MPC). The deep Koopman operator is trained on sampled flight data to construct a high-dimensional latent representation in which the nonlinear quadrotor dynamics are approximated by linear models. This linearization enables the application of MPC to efficiently optimize control actions over a finite prediction horizon, ensuring accurate trajectory t…

Improved Vehicle Maneuver Prediction using Game Theoretic Priors
Nishant Doshi
https://arxiv.org/abs/2509.21873 https://arxiv.org/pdf/2509.21873

Improved Vehicle Maneuver Prediction using Game Theoretic Priors
Conventional maneuver prediction methods use some sort of classification model on temporal trajectory data to predict behavior of agents over a set time horizon. Despite of having the best precision and recall, these models cannot predict a lane change accurately unless they incorporate information about the entire scene. Level-k game theory can leverage the human-like hierarchical reasoning to come up with the most rational decisions each agent can make in a group. This can be leveraged to mod…

AERO-MPPI: Anchor-Guided Ensemble Trajectory Optimization for Agile Mapless Drone Navigation
Xin Chen, Rui Huang, Longbin Tang, Lin Zhao
https://arxiv.org/abs/2509.17340 https:/…

AERO-MPPI: Anchor-Guided Ensemble Trajectory Optimization for Agile Mapless Drone Navigation
Agile mapless navigation in cluttered 3D environments poses significant challenges for autonomous drones. Conventional mapping-planning-control pipelines incur high computational cost and propagate estimation errors. We present AERO-MPPI, a fully GPU-accelerated framework that unifies perception and planning through an anchor-guided ensemble of Model Predictive Path Integral (MPPI) optimizers. Specifically, we design a multi-resolution LiDAR point-cloud representation that rapidly extracts spat…

ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
Junkai Jiang, Yihe Chen, Yibin Yang, Ruochen Li, Shaobing Xu, Jianqiang Wang
https://arxiv.org/abs/2508.09581

ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
Multi-vehicle trajectory planning (MVTP) is one of the key challenges in multi-robot systems (MRSs) and has broad applications across various fields. This paper presents ESCoT, an enhanced step-based coordinate trajectory planning method for multiple car-like robots. ESCoT incorporates two key strategies: collaborative planning for local robot groups and replanning for duplicate configurations. These strategies effectively enhance the performance of step-based MVTP methods. Through extensive ex…