Tootfinder

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 (…

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…

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…

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…

Sources: Paramount Skydance, Comcast, and Netflix bid in the first round for WBD; Comcast and Netflix are seeking just the studio and streaming parts of WBD (New York Times)
https://www.nytimes.com/2025/11/20/business/media/warner-discov…

Netflix, Comcast and Paramount Submit Warner Bros. Discovery Bids
The outcome of the sale could alter the trajectory of the entertainment business.

Julguei que isto jš estaria resolvido por esta altura.
Mas não. Aparentemente estš para durar. E perigoso.
Pelo menos a avaliar pelo autêntico novelo que é o voo deste kamov.
#FogoAljezur

Training thermodynamic computers by gradient descent
Stephen Whitelam
https://arxiv.org/abs/2509.15324 https://arxiv.org/pdf/2509.15324

Training thermodynamic computers by gradient descent
We show how to adjust the parameters of a thermodynamic computer by gradient descent in order to perform a desired computation at a specified observation time. Within a digital simulation of a thermodynamic computer, training proceeds by maximizing the probability with which the computer would generate an idealized dynamical trajectory. The idealized trajectory is designed to reproduce the activations of a neural network trained to perform the desired computation. This teacher-student scheme re…

Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
Zhiyu Mou, Yiqin Lv, Miao Xu, Cheems Wang, Yixiu Mao, Qichen Ye, Chao Li, Rongquan Bai, Chuan Yu, Jian Xu, Bo Zheng
https://arxiv.org/abs/2509.15927

Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
Auto-bidding is an essential tool for advertisers to enhance their advertising performance. Recent progress has shown that AI-Generated Bidding (AIGB), which formulates the auto-bidding as a trajectory generation task and trains a conditional diffusion-based planner on offline data, achieves superior and stable performance compared to typical offline reinforcement learning (RL)-based auto-bidding methods. However, existing AIGB methods still encounter a performance bottleneck due to their negle…

PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models
Ruiqi Wang, Dezhong Zhao, Ziqin Yuan, Tianyu Shao, Guohua Chen, Dominic Kao, Sungeun Hong, Byung-Cheol Min
https://arxiv.org/abs/2509.15607

PRIMT: Preference-based Reinforcement Learning with Multimodal Feedback and Trajectory Synthesis from Foundation Models
Preference-based reinforcement learning (PbRL) has emerged as a promising paradigm for teaching robots complex behaviors without reward engineering. However, its effectiveness is often limited by two critical challenges: the reliance on extensive human input and the inherent difficulties in resolving query ambiguity and credit assignment during reward learning. In this paper, we introduce PRIMT, a PbRL framework designed to overcome these challenges by leveraging foundation models (FMs) for mul…

Classical first passage problems for $p$-adic stochastic processes
A. Kh. Bikulov, A. P. Zubarev
https://arxiv.org/abs/2509.16096 https://arxiv.org/pdf/250…

Classical first passage problems for $p$-adic stochastic processes
In this paper we present a comprehensive analysis of the solution of the classical problem of finding the distribution density of a random variable - the first passage time to a given domain by the trajectory of a $p$-adic Markov stochastic process with probability density function satisfying the solution of the Cauchy problem for the Vladimirov equation (a $p$-adic analog of the Kolmogorov-Feller equation with the kernel of the Vladimirov operator) with uniform initial distribution in the unit…

Replaced article(s) found for math.OC. https://arxiv.org/list/math.OC/new
[1/1]:
- A Universal Birkhoff Theory for Fast Trajectory Optimization
I. M. Ross
https…

Cowboys' Quinnen Williams makes presence felt 'immediately', alters Dallas' defensive trajectory

https://www.cbssports.com/nfl/news/cowboys-quinnen-williams-…

Now we have the final accounting from the Congressional Budget Office:
DOGE didn’t just fail to cut spending
—it presided over spending increases that exceeded even pre-DOGE projections.
https://www.techdirt.com/2025/10/14/musk-p

Musk Promised To Cut $2 Trillion. Government Spending Went Up Instead
Elon Musk’s promises on government spending cuts have followed a predictable trajectory: slash $2 trillion, then $1 trillion, then a mere $150 billion. Now we have the final accounting from t…

Zak Phase Dislocations in Trimer Lattices
Tileubek Uakhitov, Abdybek Urmanov, Serik E. Kumekov, Anton S. Desyatnikov
https://arxiv.org/abs/2509.15894 https://

Zak Phase Dislocations in Trimer Lattices
Wave propagation in periodic media is governed by energy-momentum relation and geometric phases characterizing band topology, such as Zak phase in one-dimensional lattices. We demonstrate that in the off-diagonal trimer lattices Zak phase carries quantized screw-type dislocations winding around degeneracies in parameter space. If the lattice evolves in time periodically, as in adiabatic Thouless pump, corresponding closed trajectory in parameter space is characterized by a Chern number equal th…

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 …

Automated Model Tuning for Multifidelity Uncertainty Propagation in Trajectory Simulation
James E. Warner, Geoffrey F. Bomarito, Gianluca Geraci, Michael S. Eldred
https://arxiv.org/abs/2509.16007

Automated Model Tuning for Multifidelity Uncertainty Propagation in Trajectory Simulation
Multifidelity uncertainty propagation combines the efficiency of low-fidelity models with the accuracy of a high-fidelity model to construct statistical estimators of quantities of interest. It is well known that the effectiveness of such methods depends crucially on the relative correlations and computational costs of the available computational models. However, the question of how to automatically tune low-fidelity models to maximize performance remains an open area of research. This work inv…

Mapping the Urban Mobility Intelligence Frontier: A Scientometric Analysis of Data-Driven Pedestrian Trajectory Prediction and Simulation
Junhao Xu, Hui Zeng
https://arxiv.org/abs/2510.10327

Mapping the Urban Mobility Intelligence Frontier: A Scientometric Analysis of Data-Driven Pedestrian Trajectory Prediction and Simulation
Understanding and predicting pedestrian dynamics has become essential for shaping safer, more responsive, and human-centered urban environments. This study conducts a comprehensive scientometric analysis of research on data-driven pedestrian trajectory prediction and crowd simulation, mapping its intellectual evolution and interdisciplinary structure. Using bibliometric data from the Web of Science Core Collection, we employ SciExplorer and Bibliometrix to identify major trends, influential con…

An MPC framework for efficient navigation of mobile robots in cluttered environments
Johannes K\"ohler, Daniel Zhang, Raffaele Soloperto, Andrea Carron, Melanie Zeilinger
https://arxiv.org/abs/2509.15917

An MPC framework for efficient navigation of mobile robots in cluttered environments
We present a model predictive control (MPC) framework for efficient navigation of mobile robots in cluttered environments. The proposed approach integrates a finite-segment shortest path planner into the finite-horizon trajectory optimization of the MPC. This formulation ensures convergence to dynamically selected targets and guarantees collision avoidance, even under general nonlinear dynamics and cluttered environments. The approach is validated through hardware experiments on a small ground …

That's so Retro: The Gaia-Sausage-Enceladus Merger Trajectory as the Origin of the Chemical Abundance Bimodality in the Milky Way Disk
James W. Johnson, Diane K. Feuillet, Ana Bonaca, Danielle de Brito Silva
https://arxiv.org/abs/2510.08688

That's so Retro: The Gaia-Sausage-Enceladus Merger Trajectory as the Origin of the Chemical Abundance Bimodality in the Milky Way Disk
The Milky Way (MW) is thought to have experienced a $\sim$3:1 mass-ratio merger event near redshift $z\sim2$ with a significantly retrograde trajectory. This now-disrupted dwarf galaxy is commonly known as the Gaia-Sausage-Enceladus (GSE). In this paper, we investigate the impact of the GSE merger trajectory on metal abundances in the MW disk. We construct numerical models of Galactic chemical evolution (GCE) incorporating radial gas flows to account for angular momentum transport during the me…

HiCoTraj:Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory
Junyi Xie, Yuankun Jiao, Jina Kim, Yao-Yi Chiang, Lingyi Zhao, Khurram Shafique
https://arxiv.org/abs/2510.12067

HiCoTraj:Zero-Shot Demographic Reasoning via Hierarchical Chain-of-Thought Prompting from Trajectory
Inferring demographic attributes such as age, sex, or income level from human mobility patterns enables critical applications such as targeted public health interventions, equitable urban planning, and personalized transportation services. Existing mobility-based demographic inference studies heavily rely on large-scale trajectory data with demographic labels, leading to limited interpretability and poor generalizability across different datasets and user groups. We propose HiCoTraj (Zero-Shot …

Marx's Theory of Value at the Frontiers Classical Political Economics, Imperialism and Ecological Breakdown
By Güney Işıkara, Patrick Mokre
I'd be interested in reading this, but £116 for 200 pages?
The piss is being taken, along with the money.
Marx's Theory of Value at the Frontiers: Classical Political Economics

Marx's Theory of Value at the Frontiers: Classical Political Economics, Imperialism and Ecological Breakdown
Why do humans produce the things they do, in the way they do it?  As this book shows, the classical political economics approach to value and prices has fundamental implications for analyzing the historical trajectory of capitalism. It demonstrates that the classical political economists’ approach to value and prices, which finds its most advanced formulation in Marx, sheds light on the source of profits, exploitation, whether equivalents are exchanged in trade, dynamics of asymmetric and

Trajectory-Protected Quantum Computing
Barbara \v{S}oda, Pierre-Antoine Graham, T. Rick Perche, Gurpahul Singh
https://arxiv.org/abs/2510.12771 https://arx…

Trajectory-Protected Quantum Computing
We introduce a novel method that simultaneously isolates a quantum computer from decoherence and enables the controlled implementation of computational gates. We demonstrate a quantum computing model that utilizes a qubit's motion to protect it from decoherence. We model a qubit interacting with a quantum field via the standard light-matter interaction model: an Unruh-DeWitt detector, i.e., the qubit, follows a prescribed classical trajectory while interacting with a scalar quantum field. We sw…

nazis. i hate these guys. https://www.reuters.com/world/us/german-far-right-lawmaker-calls-us-german-nationalist-alliance-maga-gala-2025-12-14/

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

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

Thoughts on The Curve conference, where prominent figures debated about AI progress, and why automating research engineers is plausible within years (Nathan Lambert/Interconnects)
https://www.interconnects.ai/p/thoughts-on-the-curve

Thoughts on The Curve
The conference and the trajectory.

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…

Trajectory-Dependent Electronic Energy Losses in Ion Range Simulations
Glen P. Kiely, Bruno Semi\~ao, Evgeniia Ponomareva, Rafael Nu\~nez-Palacio, Unna Arpiainen, Andrea E. Sand
https://arxiv.org/abs/2510.08422

Trajectory-Dependent Electronic Energy Losses in Ion Range Simulations
The energy losses of energetic ions in materials depend on both nuclear and electronic interactions. In channeling geometries, the stopping effect of these interactions can be highly reduced, resulting in deeper ion penetration. Comprehensive, trajectory-dependent models for ion-material interactions are therefore crucial for the accurate prediction of ion range profiles. We present the implementation of a recent electron density-dependent energy-loss model in the efficient molecular dynamics-b…


The day after Democrats swept this month’s voting, a group of jubilant Democratic congressional candidates gathered on Capitol Hill to listen to Rep. Jason Crow (D-Colorado) explain how they could triumph in next year’s midterm elections, too.

Crow, dressed in jeans, boots and an open-necked shirt, urged them to campaign next year as if they were running for mayor:
Be local, be authentic, don’t listen too much to campaign consultants, he argued,
and be ready to separate f…

This rising House Democrat is a voice for the angry middle
Why I’m keeping an eye on the political trajectory of Jason Crow, a veteran of Iraq and Afghanistan.

from my link log —
Synthetic aperture radar autofocus and calibration.
https://hforsten.com/synthetic-aperture-radar-autofocus-and-calibration.html
saved 2025-10-11

Synthetic aperture radar autofocus and calibration
3D trajectory position error estimation autofocus, antenna pattern normalization, and polarimetric calibration for drone mounted SAR radar.

Offline Reinforcement Learning with Generative Trajectory Policies
Xinsong Feng, Leshu Tang, Chenan Wang, Haipeng Chen
https://arxiv.org/abs/2510.11499 https://

Offline Reinforcement Learning with Generative Trajectory Policies
Generative models have emerged as a powerful class of policies for offline reinforcement learning (RL) due to their ability to capture complex, multi-modal behaviors. However, existing methods face a stark trade-off: slow, iterative models like diffusion policies are computationally expensive, while fast, single-step models like consistency policies often suffer from degraded performance. In this paper, we demonstrate that it is possible to bridge this gap. The key to moving beyond the limitati…

A Geomechanically-Informed Framework for Wellbore Trajectory Prediction: Integrating First-Principles Kinematics with a Rigorous Derivation of Gated Recurrent Networks
Shubham Kumar, Anshuman Sahoo
https://arxiv.org/abs/2510.07564

A Geomechanically-Informed Framework for Wellbore Trajectory Prediction: Integrating First-Principles Kinematics with a Rigorous Derivation of Gated Recurrent Networks
Accurate wellbore trajectory prediction is a paramount challenge in subsurface engineering, governed by complex interactions between the drilling assembly and heterogeneous geological formations. This research establishes a comprehensive, mathematically rigorous framework for trajectory prediction that moves beyond empirical modeling to a geomechanically-informed, data-driven surrogate approach.The study leverages Log ASCII Standard (LAS) and wellbore deviation (DEV) data from 14 wells in the G…

This guy made a Synthetic Aperture Radar (i.e. a radar that gives you camera-like pictures) mounted on a drone. Scroll to the bottom for actual sample pictures and a movie. Lots of tech detail and open-source designs and software.
https://hforsten.com/synthetic-apertur

Enhancing Long Chain-of-Thought Reasoning through Multi-Path Plan Aggregation
Siheng Xiong, Ali Payani, Faramarz Fekri
https://arxiv.org/abs/2510.11620 https://

Enhancing Long Chain-of-Thought Reasoning through Multi-Path Plan Aggregation
Inference-time scaling enhances the reasoning ability of a language model (LM) by extending its chain-of-thought (CoT). However, existing approaches typically generate the entire reasoning chain in a single forward pass, which often leads to CoT derailment, i.e., the reasoning trajectory drifting off course due to compounding errors. This problem is particularly severe for smaller LMs with long CoTs due to their limited capacity. To address this, we analyze raw long CoTs and uncover a reasoning…

Maritime Communication in Evaporation Duct Environment with Ship Trajectory Optimization
Ruifeng Gao, Hao Zhang, Jue Wang, Ye Li, Yingdong Hu, Qiuming Zhu, Shu Sun, Meixia Tao
https://arxiv.org/abs/2510.06946

Maritime Communication in Evaporation Duct Environment with Ship Trajectory Optimization
In maritime wireless networks, the evaporation duct effect has been known as a preferable condition for long-range transmissions. However, how to effectively utilize the duct effect for efficient communication design is still open for investigation. In this paper, we consider a typical scenario of ship-to-shore data transmission, where a ship collects data from multiple oceanographic buoys, sails from one to another, and transmits the collected data back to a terrestrial base station during its…

"At $4.5bn each year extreme weather is costing Australia three times as much compared with 1990s, insurers say"
#Australia #Climate #ClimateChange

"I read it over now with this kind of full-spectrum cringe of the spirit. It’s the rattle of a person going on and professorially on, quite as if the substance of a discipline, or its intellectual trajectory, or even just the nourishing joy of sustained and serious study, mattered at all to the person he was talking to, or to the majestic institution he represents. And honestly, what could be more feeble?
It’s not that those things don’t matter: they absolutely goddamn do, and will keep on mattering, and I wouldn’t go on with the whole tedious business of teaching if I thought otherwise. It’s just that they never mattered much to the Times and they are, to appearances, mattering less and less by the day. I should remember that, and so should you."
https://lithub.com/maybe-dont-talk-to-the-new-york-times-about-zohran-mamdani/

CAMNet: Leveraging Cooperative Awareness Messages for Vehicle Trajectory Prediction
Mattia Grasselli, Angelo Porrello, Carlo Augusto Grazia
https://arxiv.org/abs/2510.12703 http…

CAMNet: Leveraging Cooperative Awareness Messages for Vehicle Trajectory Prediction
Autonomous driving remains a challenging task, particularly due to safety concerns. Modern vehicles are typically equipped with expensive sensors such as LiDAR, cameras, and radars to reduce the risk of accidents. However, these sensors face inherent limitations: their field of view and line of sight can be obstructed by other vehicles, thereby reducing situational awareness. In this context, vehicle-to-vehicle communication plays a crucial role, as it enables cars to share information and rema…

New US strategy document takes hard line on Europe's 'trajectory,' NATO expansion: https://benborges.xyz/2025/12/05/new-us-strategy-document-takes.html

Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization
Simon Idoko, Arun Kumar Singh
https://arxiv.org/abs/2510.09204

Flow-Opt: Scalable Centralized Multi-Robot Trajectory Optimization with Flow Matching and Differentiable Optimization
Centralized trajectory optimization in the joint space of multiple robots allows access to a larger feasible space that can result in smoother trajectories, especially while planning in tight spaces. Unfortunately, it is often computationally intractable beyond a very small swarm size. In this paper, we propose Flow-Opt, a learning-based approach towards improving the computational tractability of centralized multi-robot trajectory optimization. Specifically, we reduce the problem to first lear…

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…

Agent-Based Simulation of a Financial Market with Large Language Models
Ryuji Hashimoto, Takehiro Takayanagi, Masahiro Suzuki, Kiyoshi Izumi
https://arxiv.org/abs/2510.12189 htt…

Agent-Based Simulation of a Financial Market with Large Language Models
In real-world stock markets, certain chart patterns -- such as price declines near historical highs -- cannot be fully explained by fundamentals alone. These phenomena suggest the presence of path dependence in price formation, where investor decisions are influenced not only by current market conditions but also by the trajectory of prices leading up to the present. Path dependence has drawn attention in behavioral finance as a key mechanism behind such anomalies. One plausible driver of path …

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 …

Micah Parsons and Davante Adams injuries have opened up NFC; Seahawks, 49ers and Bears among the beneficiaries

https://www.cbssports.com/nfl/news/micah-parsons-and…

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…

ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving
Zhiyu Zheng, Shaoyu Chen, Haoran Yin, Xinbang Zhang, Jialv Zou, Xinggang Wang, Qian Zhang, Lefei Zhang
https://arxiv.org/abs/2510.08562

ResAD: Normalized Residual Trajectory Modeling for End-to-End Autonomous Driving
End-to-end autonomous driving (E2EAD) systems, which learn to predict future trajectories directly from sensor data, are fundamentally challenged by the inherent spatio-temporal imbalance of trajectory data. This imbalance creates a significant optimization burden, causing models to learn spurious correlations instead of causal inference, while also prioritizing uncertain, distant predictions, thereby compromising immediate safety. To address these issues, we propose ResAD, a novel Normalized R…

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Jushan Chen, Santiago Paternain
https://arxiv.org/abs/2510.04436 https://arxiv.o…

PAD-TRO: Projection-Augmented Diffusion for Direct Trajectory Optimization
Recently, diffusion models have gained popularity and attention in trajectory optimization due to their capability of modeling multi-modal probability distributions. However, addressing nonlinear equality constraints, i.e, dynamic feasi- bility, remains a great challenge in diffusion-based trajectory optimization. Recent diffusion-based trajectory optimization frameworks rely on a single-shooting style approach where the denoised control sequence is applied to forward propagate the dynamical sy…

Riccati-ZORO: An efficient algorithm for heuristic online optimization of internal feedback laws in robust and stochastic model predictive control
Florian Messerer, Yunfan Gao, Jonathan Frey, Moritz Diehl
https://arxiv.org/abs/2511.10473 https://arxiv.org/pdf/2511.10473 https://arxiv.org/html/2511.10473
arXiv:2511.10473v1 Announce Type: new
Abstract: We present Riccati-ZORO, an algorithm for tube-based optimal control problems (OCP). Tube OCPs predict a tube of trajectories in order to capture predictive uncertainty. The tube induces a constraint tightening via additional backoff terms. This backoff can significantly affect the performance, and thus implicitly defines a cost of uncertainty. Optimizing the feedback law used to predict the tube can significantly reduce the backoffs, but its online computation is challenging.
Riccati-ZORO jointly optimizes the nominal trajectory and uncertainty tube based on a heuristic uncertainty cost design. The algorithm alternates between two subproblems: (i) a nominal OCP with fixed backoffs, (ii) an unconstrained tube OCP, which optimizes the feedback gains for a fixed nominal trajectory. For the tube optimization, we propose a cost function informed by the proximity of the nominal trajectory to constraints, prioritizing reduction of the corresponding backoffs. These ideas are developed in detail for ellipsoidal tubes under linear state feedback. In this case, the decomposition into the two subproblems yields a substantial reduction of the computational complexity with respect to the state dimension from $\mathcal{O}(n_x^6)$ to $\mathcal{O}(n_x^3)$, i.e., the complexity of a nominal OCP.
We investigate the algorithm in numerical experiments, and provide two open-source implementations: a prototyping version in CasADi and a high-performance implementation integrated into the acados OCP solver.
toXiv_bot_toot

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…

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…

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 …

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…

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Curving the Trajectory of Light in Refraction Index Gradients
Oliver Polachini, Fabricio Marques
https://arxiv.org/abs/2510.03615 https://arxiv.org/pdf/251…

Curving the Trajectory of Light in Refraction Index Gradients
This work seeks to present an investigation about the trajectories followed by rays of light passing through refractive index gradients that was entirely carried by high school students. Such trajectories are curved, therefore contradicting the common sense that light should always travel along straight lines. This fact causes the formation of distorted and striated images, similarly to a type of mirage known as Fata Morgana. Using a rectangular aquarium containing solutions with different grad…

Controlling Intent Expressiveness in Robot Motion with Diffusion Models
Wenli Shi, Clemence Grislain, Olivier Sigaud, Mohamed Chetouani
https://arxiv.org/abs/2510.12370 https://…

Controlling Intent Expressiveness in Robot Motion with Diffusion Models
Legibility of robot motion is critical in human-robot interaction, as it allows humans to quickly infer a robot's intended goal. Although traditional trajectory generation methods typically prioritize efficiency, they often fail to make the robot's intentions clear to humans. Meanwhile, existing approaches to legible motion usually produce only a single "most legible" trajectory, overlooking the need to modulate intent expressiveness in different contexts. In this work, we propose a novel motio…

BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
Junyi Xie, Jina Kim, Yao-Yi Chiang, Lingyi Zhao, Khurram Shafique
https://arxiv.org/abs/2510.12076

BeSTAD: Behavior-Aware Spatio-Temporal Anomaly Detection for Human Mobility Data
Traditional anomaly detection in human mobility has primarily focused on trajectory-level analysis, identifying statistical outliers or spatiotemporal inconsistencies across aggregated movement traces. However, detecting individual-level anomalies, i.e., unusual deviations in a person's mobility behavior relative to their own historical patterns, within datasets encompassing large populations remains a significant challenge. In this paper, we present BeSTAD (Behavior-aware Spatio-Temporal Anoma…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[9/14]:
- Hyper-STTN: Hypergraph Augmented Spatial-Temporal Transformer Network for Trajectory Prediction
Weizheng Wang, Baijian Yang, Sungeun Hong, Wenhai Sun, Byung-Cheol Min

Trajectory Conditioned Cross-embodiment Skill Transfer
YuHang Tang, Yixuan Lou, Pengfei Han, Haoming Song, Xinyi Ye, Dong Wang, Bin Zhao
https://arxiv.org/abs/2510.07773 https:/…

Trajectory Conditioned Cross-embodiment Skill Transfer
Learning manipulation skills from human demonstration videos presents a promising yet challenging problem, primarily due to the significant embodiment gap between human body and robot manipulators. Existing methods rely on paired datasets or hand-crafted rewards, which limit scalability and generalization. We propose TrajSkill, a framework for Trajectory Conditioned Cross-embodiment Skill Transfer, enabling robots to acquire manipulation skills directly from human demonstration videos. Our key …

Trajectory-Optimized Density Control with Flow Matching
Xu Duan, Dongmei Chen
https://arxiv.org/abs/2510.06666 https://arxiv.org/pdf/2510.06666

Trajectory-Optimized Density Control with Flow Matching
Optimal transport (OT) and Schr{ö}dinger bridge (SB) problems have emerged as powerful frameworks for transferring probability distributions with minimal cost. However, existing approaches typically focus on endpoint matching while neglecting critical path-dependent properties -- particularly collision avoidance in multiagent systems -- which limits their practical applicability in robotics, economics, and other domains where inter-agent interactions are essential. Moreover, traditional densit…

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…

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 …

GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
Alexander Du, Emre Adabag, Gabriel Bravo, Brian Plancher
https://arxiv.org/abs/2510.07625

GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
While Model Predictive Control (MPC) delivers strong performance across robotics applications, solving the underlying (batches of) nonlinear trajectory optimization (TO) problems online remains computationally demanding. Existing GPU-accelerated approaches typically (i) parallelize a single solve to meet real-time deadlines, (ii) scale to very large batches at slower-than-real-time rates, or (iii) achieve speed by restricting model generality (e.g., point-mass dynamics or a single linearization…

Measurement-induced entanglement in noisy 2D random Clifford circuits
Zhi-Yuan Wei, Jon Nelson, Joel Rajakumar, Esther Cruz, Alexey V. Gorshkov, Michael J. Gullans, Daniel Malz
https://arxiv.org/abs/2510.12743

Measurement-induced entanglement in noisy 2D random Clifford circuits
We study measurement-induced entanglement generated by column-by-column sampling of noisy 2D random Clifford circuits of size $N$ and depth $T$. Focusing on the operator entanglement $S_{\rm op}$ of the sampling-induced boundary state, first, we reproduce in the noiseless limit a finite-depth transition from area- to volume-law scaling. With on-site probablistic trace noise at any constant rate $p>0$, the maximal $S_{\rm op}$ attained along the sampling trajectory obeys an area law in the bound…

Laminar: A Scalable Asynchronous RL Post-Training Framework
Guangming Sheng, Yuxuan Tong, Borui Wan, Wang Zhang, Chaobo Jia, Xibin Wu, Yuqi Wu, Xiang Li, Chi Zhang, Yanghua Peng, Haibin Lin, Xin Liu, Chuan Wu
https://arxiv.org/abs/2510.12633

Laminar: A Scalable Asynchronous RL Post-Training Framework
Reinforcement learning (RL) post-training for Large Language Models (LLMs) is now scaling to large clusters and running for extended durations to enhance model reasoning performance. However, the scalability of existing RL frameworks is limited, as extreme long-tail skewness in RL trajectory generation causes severe GPU underutilization. Current asynchronous RL systems attempt to mitigate this, but they rely on global weight synchronization between the actor and all rollouts, which creates a ri…

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…

Latent Diffusion Unlearning: Protecting Against Unauthorized Personalization Through Trajectory Shifted Perturbations
Naresh Kumar Devulapally, Shruti Agarwal, Tejas Gokhale, Vishnu Suresh Lokhande
https://arxiv.org/abs/2510.03089

Latent Diffusion Unlearning: Protecting Against Unauthorized Personalization Through Trajectory Shifted Perturbations
Text-to-image diffusion models have demonstrated remarkable effectiveness in rapid and high-fidelity personalization, even when provided with only a few user images. However, the effectiveness of personalization techniques has lead to concerns regarding data privacy, intellectual property protection, and unauthorized usage. To mitigate such unauthorized usage and model replication, the idea of generating ``unlearnable'' training samples utilizing image poisoning techniques has emerged. Existing…

Auto-scaling Continuous Memory for GUI Agent
Wenyi Wu, Kun Zhou, Ruoxin Yuan, Vivian Yu, Stephen Wang, Zhiting Hu, Biwei Huang
https://arxiv.org/abs/2510.09038 https://

Auto-scaling Continuous Memory for GUI Agent
We study how to endow GUI agents with scalable memory that help generalize across unfamiliar interfaces and long-horizon tasks. Prior GUI agents compress past trajectories into text tokens, which balloons context length and misses decisive visual cues (e.g., exact widget size and position). We propose a continuous memory that encodes each GUI trajectory into a fixed-length sequence of continuous embeddings using the VLM itself as an encoder; these embeddings are plugged directly into the backbo…

A Trajectory Generator for High-Density Traffic and Diverse Agent-Interaction Scenarios
Ruining Yang, Yi Xu, Yixiao Chen, Yun Fu, Lili Su
https://arxiv.org/abs/2510.02627 https:…

A Trajectory Generator for High-Density Traffic and Diverse Agent-Interaction Scenarios
Accurate trajectory prediction is fundamental to autonomous driving, as it underpins safe motion planning and collision avoidance in complex environments. However, existing benchmark datasets suffer from a pronounced long-tail distribution problem, with most samples drawn from low-density scenarios and simple straight-driving behaviors. This underrepresentation of high-density scenarios and safety critical maneuvers such as lane changes, overtaking and turning is an obstacle to model generaliza…

Data-Driven Estimation of Quadrotor Motor Efficiency via Residual Minimization
Sheng-Wen Cheng, Teng-Hu Cheng
https://arxiv.org/abs/2510.11388 https://arxi…

Data-Driven Estimation of Quadrotor Motor Efficiency via Residual Minimization
A data-driven framework is proposed for online estimation of quadrotor motor efficiency via residual minimization. The problem is formulated as a constrained nonlinear optimization that minimizes trajectory residuals between measured flight data and predictions generated by a quadrotor dynamics model. A sliding-window strategy enables online estimation, and the optimization is efficiently solved using an iteratively reweighted least squares (IRLS) scheme combined with a primal-dual interior-poi…

Learning-based model predictive control with moving horizon state estimation for autonomous racing
Yassine Kebbati, Andreas Rauh, Naima Ait-Oufroukh, Dalil Ichalal, Vincent Vigneron
https://arxiv.org/abs/2510.05366

Learning-based model predictive control with moving horizon state estimation for autonomous racing
This paper addresses autonomous racing by introducing a real-time nonlinear model predictive controller (NMPC) coupled with a moving horizon estimator (MHE). The racing problem is solved by an NMPC-based off-line trajectory planner that computes the best trajectory while considering the physical limits of the vehicle and circuit constraints. The developed controller is further enhanced with a learning extension based on Gaussian process regression that improves model predictions. The proposed c…

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…

Advancing End-to-End Pixel Space Generative Modeling via Self-supervised Pre-training
Jiachen Lei, Keli Liu, Julius Berner, Haiming Yu, Hongkai Zheng, Jiahong Wu, Xiangxiang Chu
https://arxiv.org/abs/2510.12586

Advancing End-to-End Pixel Space Generative Modeling via Self-supervised Pre-training
Pixel-space generative models are often more difficult to train and generally underperform compared to their latent-space counterparts, leaving a persistent performance and efficiency gap. In this paper, we introduce a novel two-stage training framework that closes this gap for pixel-space diffusion and consistency models. In the first stage, we pre-train encoders to capture meaningful semantics from clean images while aligning them with points along the same deterministic sampling trajectory, …

Optimal Smooth Coverage Trajectory Planning for Quadrotors in Cluttered Environment
Duanjiao Li, Yun Chen, Ying Zhang, Junwen Yao, Dongyue Huang, Jianguo Zhang, Ning Ding
https://arxiv.org/abs/2510.03169

Optimal Smooth Coverage Trajectory Planning for Quadrotors in Cluttered Environment
For typical applications of UAVs in power grid scenarios, we construct the problem as planning UAV trajectories for coverage in cluttered environments. In this paper, we propose an optimal smooth coverage trajectory planning algorithm. The algorithm consists of two stages. In the front-end, a Genetic Algorithm (GA) is employed to solve the Traveling Salesman Problem (TSP) for Points of Interest (POIs), generating an initial sequence of optimized visiting points. In the back-end, the sequence is…

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…

Work Zones challenge VLM Trajectory Planning: Toward Mitigation and Robust Autonomous Driving
Yifan Liao, Zhen Sun, Xiaoyun Qiu, Zixiao Zhao, Wenbing Tang, Xinlei He, Xinhu Zheng, Tianwei Zhang, Xinyi Huang, Xingshuo Han
https://arxiv.org/abs/2510.02803

Work Zones challenge VLM Trajectory Planning: Toward Mitigation and Robust Autonomous Driving
Visual Language Models (VLMs), with powerful multimodal reasoning capabilities, are gradually integrated into autonomous driving by several automobile manufacturers to enhance planning capability in challenging environments. However, the trajectory planning capability of VLMs in work zones, which often include irregular layouts, temporary traffic control, and dynamically changing geometric structures, is still unexplored. To bridge this gap, we conduct the \textit{first} systematic study of VLM…

Controller for Incremental Input-to-State Practical Stabilization of Partially Unknown systems with Invariance Guarantees
P Sangeerth, David Smith Sundarsingh, Bhabani Shankar Dey, Pushpak Jagtap
https://arxiv.org/abs/2510.10450

Controller for Incremental Input-to-State Practical Stabilization of Partially Unknown systems with Invariance Guarantees
Incremental stability is a property of dynamical systems that ensures the convergence of trajectories with respect to each other rather than a fixed equilibrium point or a fixed trajectory. In this paper, we introduce a related stability notion called incremental input-to-state practical stability (δ-ISpS), ensuring safety guarantees. We also present a feedback linearization based control design scheme that renders a partially unknown system incrementally input-to-state practically stable and …

Real-Time Nonlinear Model Predictive Control of Heavy-Duty Skid-Steered Mobile Platform for Trajectory Tracking Tasks
Alvaro Paz, Pauli Mustalahti, Mohammad Dastranj, Jouni Mattila
https://arxiv.org/abs/2510.02976

Real-Time Nonlinear Model Predictive Control of Heavy-Duty Skid-Steered Mobile Platform for Trajectory Tracking Tasks
This paper presents a framework for real-time optimal controlling of a heavy-duty skid-steered mobile platform for trajectory tracking. The importance of accurate real-time performance of the controller lies in safety considerations of situations where the dynamic system under control is affected by uncertainties and disturbances, and the controller should compensate for such phenomena in order to provide stable performance. A multiple-shooting nonlinear model-predictive control framework is pr…

Long-Time Analysis of Stochastic Heavy Ball Dynamics for Convex Optimization and Monotone Equations
Radu Ioan Bot, Chiara Schindler
https://arxiv.org/abs/2510.02951 https://

Long-Time Analysis of Stochastic Heavy Ball Dynamics for Convex Optimization and Monotone Equations
In a separable real Hilbert space, we study the problem of minimizing a convex function with Lipschitz continuous gradient in the presence of noisy evaluations. To this end, we associate a stochastic Heavy Ball system, incorporating a friction coefficient, with the optimization problem. We establish existence and uniqueness of trajectory solutions for this system. Under a square integrability condition for the diffusion term, we prove almost sure convergence of the trajectory process to an opti…

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. …

Performance Index Shaping for Closed-loop Optimal Control
Ayush Rai, Shaoshuai Mou, Brian D. O. Anderson
https://arxiv.org/abs/2510.10202 https://arxiv.org…

Performance Index Shaping for Closed-loop Optimal Control
The design of the performance index, also referred to as cost or reward shaping, is central to both optimal control and reinforcement learning, as it directly determines the behaviors, trade-offs, and objectives that the resulting control laws seek to achieve. A commonly used approach for this inference task in recent years is differentiable trajectory optimization, which allows gradients to be computed with respect to cost parameters by differentiating through an optimal control solver. Howeve…

EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
Raghav Mishra, Ian R. Manchester
https://arxiv.org/abs/2510.07700

EB-MBD: Emerging-Barrier Model-Based Diffusion for Safe Trajectory Optimization in Highly Constrained Environments
We propose enforcing constraints on Model-Based Diffusion by introducing emerging barrier functions inspired by interior point methods. We show that constraints on Model-Based Diffusion can lead to catastrophic performance degradation, even on simple 2D systems due to sample inefficiency in the Monte Carlo approximation of the score function. We introduce Emerging-Barrier Model-Based Diffusion (EB-MBD) which uses progressively introduced barrier constraints to avoid these problems, significantl…

Diffusing Trajectory Optimization Problems for Recovery During Multi-Finger Manipulation
Abhinav Kumar, Fan Yang, Sergio Aguilera Marinovic, Soshi Iba, Rana Soltani Zarrin, Dmitry Berenson
https://arxiv.org/abs/2510.07030

Diffusing Trajectory Optimization Problems for Recovery During Multi-Finger Manipulation
Multi-fingered hands are emerging as powerful platforms for performing fine manipulation tasks, including tool use. However, environmental perturbations or execution errors can impede task performance, motivating the use of recovery behaviors that enable normal task execution to resume. In this work, we take advantage of recent advances in diffusion models to construct a framework that autonomously identifies when recovery is necessary and optimizes contact-rich trajectories to recover. We use …

Latent-Feature-Informed Neural ODE Modeling for Lightweight Stability Evaluation of Black-box Grid-Tied Inverters
Jialin Zheng, Zhong Liu, Xiaonan Lu
https://arxiv.org/abs/2510.09826

Latent-Feature-Informed Neural ODE Modeling for Lightweight Stability Evaluation of Black-box Grid-Tied Inverters
Stability evaluation of black-box grid-tied inverters is vital for grid reliability, yet identification techniques are both data-hungry and blocked by proprietary internals. {To solve this, this letter proposes a latent-feature-informed neural ordinary differential equation (LFI-NODE) modeling method that can achieve lightweight stability evaluation directly from trajectory data.} LFI-NODE parameterizes the entire system ODE with a single continuous-time neural network, allowing each new sample…

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…

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…

On the optimization dynamics of RLVR: Gradient gap and step size thresholds
Joe Suk, Yaqi Duan
https://arxiv.org/abs/2510.08539 https://arxiv.org/pdf/2510.…

On the optimization dynamics of RLVR: Gradient gap and step size thresholds
Reinforcement Learning with Verifiable Rewards (RLVR), which uses simple binary feedback to post-train large language models, has shown significant empirical success. However, a principled understanding of why it works has been lacking. This paper builds a theoretical foundation for RLVR by analyzing its training process at both the full-response (trajectory) and token levels. Central to our analysis is a quantity called the Gradient Gap, which formalizes the direction of improvement from low-r…

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…

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 …

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…

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…

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…

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…

Learning Robust Agile Flight Control with Stability Guarantees
Lukas Pries, Markus Ryll
https://arxiv.org/abs/2510.12611 https://arxiv.org/pdf/2510.12611…

Learning Robust Agile Flight Control with Stability Guarantees
In the evolving landscape of high-speed agile quadrotor flight, achieving precise trajectory tracking at the platform's operational limits is paramount. Controllers must handle actuator constraints, exhibit robustness to disturbances, and remain computationally efficient for safety-critical applications. In this work, we present a novel neural-augmented feedback controller for agile flight control. The controller addresses individual limitations of existing state-of-the-art control paradigms an…

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…

NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning
Neil C. Janwani, Varun Madabushi, Maegan Tucker
https://arxiv.org/abs/2510.11542 https…

NaviGait: Navigating Dynamically Feasible Gait Libraries using Deep Reinforcement Learning
Reinforcement learning (RL) has emerged as a powerful method to learn robust control policies for bipedal locomotion. Yet, it can be difficult to tune desired robot behaviors due to unintuitive and complex reward design. In comparison, offline trajectory optimization methods, like Hybrid Zero Dynamics, offer more tuneable, interpretable, and mathematically grounded motion plans for high-dimensional legged systems. However, these methods often remain brittle to real-world disturbances like exter…

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…

Calibrated Dynamic Modeling for Force and Payload Estimation in Hydraulic Machinery
Lennart Werner, Pol Eyschen, Sean Costello, Pierluigi Micarelli, Marco Hutter
https://arxiv.org/abs/2510.11574

Calibrated Dynamic Modeling for Force and Payload Estimation in Hydraulic Machinery
Accurate real-time estimation of end effector interaction forces in hydraulic excavators is a key enabler for advanced automation in heavy machinery. Accurate knowledge of these forces allows improved, precise grading and digging maneuvers. To address these challenges, we introduce a high-accuracy, retrofittable 2D force- and payload estimation algorithm that does not impose additional requirements on the operator regarding trajectory, acceleration or the use of the slew joint. The approach is …

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…

Robust Visual Teach-and-Repeat Navigation with Flexible Topo-metric Graph Map Representation
Jikai Wang, Yunqi Cheng, Kezhi Wang, Zonghai Chen
https://arxiv.org/abs/2510.09089 h…

Robust Visual Teach-and-Repeat Navigation with Flexible Topo-metric Graph Map Representation
Visual Teach-and-Repeat Navigation is a direct solution for mobile robot to be deployed in unknown environments. However, robust trajectory repeat navigation still remains challenged due to environmental changing and dynamic objects. In this paper, we propose a novel visual teach-and-repeat navigation system, which consists of a flexible map representation, robust map matching and a map-less local navigation module. During the teaching process, the recorded keyframes are formulated as a topo-me…

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
David Nguyen, Zulfiqar Zaidi, Kevin Karol, Jessica Hodgins, Zhaoming Xie
https://arxiv.org/abs/2510.08754

Whole Body Model Predictive Control for Spin-Aware Quadrupedal Table Tennis
Developing table tennis robots that mirror human speed, accuracy, and ability to predict and respond to the full range of ball spins remains a significant challenge for legged robots. To demonstrate these capabilities we present a system to play dynamic table tennis for quadrupedal robots that integrates high speed perception, trajectory prediction, and agile control. Our system uses external cameras for high-speed ball localization, physical models with learned residuals to infer spin and pred…

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…