Tootfinder

HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention
Xiaolong Tang, Meina Kan, Shiguang Shan, Zhilong Ji, Jinfeng Bai, Xilin Chen
https://arxiv.org/abs/2404.06351

HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention
Predicting the trajectories of road agents is essential for autonomous driving systems. The recent mainstream methods follow a static paradigm, which predicts the future trajectory by using a fixed duration of historical frames. These methods make the predictions independently even at adjacent time steps, which leads to potential instability and temporal inconsistency. As successive time steps have largely overlapping historical frames, their forecasting should have intrinsic correlation, such …

High-Performance Privacy-Preserving Matrix Completion for Trajectory Recovery
Jiahao Guo, An-Bao Xu
https://arxiv.org/abs/2405.05789 https://

High-Performance Privacy-Preserving Matrix Completion for Trajectory Recovery
Matrix completion has important applications in trajectory recovery and mobile social networks. However, sending raw data containing personal, sensitive information to cloud computing nodes may lead to privacy exposure issue.The privacy-preserving matrix completion is a useful approach to perform matrix completion while preserving privacy. In this paper, we propose a high-performance method for privacy-preserving matrix completion. First,we use a lightweight encryption scheme to encrypt the raw…

Experimenting with integrating #scikitmobility #privacy checks into #Trajectools 👩‍💻
Curious? Check out the dev version in:

GitHub - movingpandas/qgis-processing-trajectory at privacy
Trajectory tools for the QGIS Processing toolbox. Contribute to movingpandas/qgis-processing-trajectory development by creating an account on GitHub.

This https://arxiv.org/abs/2312.03452 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qu…

Telling different unravelings apart via nonlinear quantum-trajectory averages
The Gorini-Kossakowski-Sudarshan-Lindblad master equation (ME) governs the density matrix of open quantum systems (OQSs). When an OQS is subjected to weak continuous measurement, its state evolves as a stochastic quantum trajectory, whose statistical average solves the ME. The ensemble of such trajectories is termed an unraveling of the ME. We propose a method to operationally distinguish unravelings produced by the same ME in different measurement scenarios, using nonlinear averages of observa…

AI-MOLE: Autonomous Iterative Motion Learning for Unknown Nonlinear Dynamics with Extensive Experimental Validation
Michael Meindl, Simon Bachhuber, Thomas Seel
https://arxiv.org/abs/2404.06179

AI-MOLE: Autonomous Iterative Motion Learning for Unknown Nonlinear Dynamics with Extensive Experimental Validation
This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method that enables systems with unknown, nonlinear dynamics to autonomously learn to solve reference tracking tasks. The method iteratively applies an input trajectory to the unknown dynamics, trains a Gaussian process model based on the experimental data, and utilizes the model to update the input trajectory until desired tracking performance is achieved. Unlike existing approaches, the proposed method determines necessary p…

"How we view the present and future of Artificial Intelligence probably turns on what we think about the current state of capitalism. Is A.I. going to inevitably improve (because markets)? Or is A.I. inevitably going to hollow out industries while providing shittier services (because markets)?"
(Original title: Are large language models on the trajectory of word processing or digital advertising?)

Are large language models on the trajectory of word processing or digital advertising?
(In which Dave stumbles upon another analogy.)

Planning with Probabilistic Opacity and Transparency: A Computational Model of Opaque/Transparent Observations
Sumukha Udupa, Jie Fu
https://arxiv.org/abs/2405.05408

Planning with Probabilistic Opacity and Transparency: A Computational Model of Opaque/Transparent Observations
Qualitative opacity of a secret is a security property, which means that a system trajectory satisfying the secret is observation-equivalent to a trajectory violating the secret. In this paper, we study how to synthesize a control policy that maximizes the probability of a secret being made opaque against an eavesdropping attacker/observer, while subject to other task performance constraints. In contrast to existing belief-based approach for opacity-enforcement, we develop an approach that uses…

This https://arxiv.org/abs/2302.04748 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Newton's Method for Global Free Flight Trajectory Optimization
Globally optimal free flight trajectory optimization can be achieved with a combination of discrete and continuous optimization. A key requirement is that Newton's method for continuous optimization converges in a sufficiently large neighborhood around a minimizer. We show in this paper that, under certain assumptions, this is the case.

This https://arxiv.org/abs/2403.04900 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Almost Global Asymptotic Trajectory Tracking for Fully-Actuated Mechanical Systems on Homogeneous Riemannian Manifolds
In this work, we address the design of tracking controllers that drive a mechanical system's state asymptotically towards a reference trajectory. Motivated by aerospace and robotics applications, we consider fully-actuated systems evolving on the broad class of homogeneous spaces (encompassing all vector spaces, Lie groups, and spheres of any finite dimension). In this setting, the transitive action of a Lie group on the configuration manifold enables an intrinsic description of the tracking er…

Web Intelligence Journal in perspective: an analysis of its two decades trajectory
Diogenes Ademir Domingos, Victor Emanuel Santos Moura, Antonio Fernando Lavareda Jacob Junior, Fabio Manoel Franca Lobato
https://arxiv.org/abs/2405.05129

Web Intelligence Journal in perspective: an analysis of its two decades trajectory
The evolution of a thematic area undergoes various changes of perspective and adopts new theoretical approaches that arise from the interactions of the community and a wide range of social needs. The advent of digital technologies, such as social networks, underlines this factor by spreading knowledge and forging links between different communities. Web intelligence is now on the verge of raising questions that broaden the understanding of how artificial intelligence impacts the Web of People, …

Planning with Probabilistic Opacity and Transparency: A Computational Model of Opaque/Transparent Observations
Sumukha Udupa, Jie Fu
https://arxiv.org/abs/2405.05408

Planning with Probabilistic Opacity and Transparency: A Computational Model of Opaque/Transparent Observations
Qualitative opacity of a secret is a security property, which means that a system trajectory satisfying the secret is observation-equivalent to a trajectory violating the secret. In this paper, we study how to synthesize a control policy that maximizes the probability of a secret being made opaque against an eavesdropping attacker/observer, while subject to other task performance constraints. In contrast to existing belief-based approach for opacity-enforcement, we develop an approach that uses…

Just Wing It: Optimal Estimation of Missing Mass in a Markovian Sequence
Ashwin Pananjady, Vidya Muthukumar, Andrew Thangaraj
https://arxiv.org/abs/2404.05819

Just Wing It: Optimal Estimation of Missing Mass in a Markovian Sequence
We study the problem of estimating the stationary mass -- also called the unigram mass -- that is missing from a single trajectory of a discrete-time, ergodic Markov chain. This problem has several applications -- for example, estimating the stationary missing mass is critical for accurately smoothing probability estimates in sequence models. While the classical Good--Turing estimator from the 1950s has appealing properties for i.i.d. data, it is known to be biased in the Markov setting, and ot…

Fifth Generation IMC: Expanding the scope to Profit, People, and the Planet
Stewart Pearson, Edward Malthouse
https://arxiv.org/abs/2404.04740 https://

Fifth Generation IMC: Expanding the scope to Profit, People, and the Planet
This editorial outlines an expanded scope for the next (fifth) generation of integrated marketing communication. It identifies key market forces that gave rise to this evolution and describes a trajectory of where Integrated Marketing Communication (IMC) has been and where it is going. The central shift is moving from primarily focusing on one stakeholder to multiple ones, including people (employees and society), the planet (environment), and profits. It identifies examples from industry that …

GLANCE -- Gravitational Lensing Authenticator using Non-Modelled Cross-Correlation Exploration of Gravitational Wave Signals
Aniruddha Chakraborty, Suvodip Mukherjee
https://arxiv.org/abs/2403.03982

GLANCE -- Gravitational Lensing Authenticator using Non-Modelled Cross-Correlation Exploration of Gravitational Wave Signals
Gravitational lensing is the effect on a lightlike trajectory by the presence of matter, affecting its trajectory in spacetime. Gravitational lensing of gravitational waves can occur in geometric optics limit (when GW wavelength is much smaller than the Schwarzschild radius of the lens i.e. $λ_{GW} \ll$ R$^{\rm sc}_{\rm lens}$, multiple images with different magnifications are formed) known as strong-lensing or in wave optics limit (when the wavelength of GW is larger than the Schwarzschild ra…

Nanometer Scanning with Micrometer Sensing: Beating Quantization Constraints in Lissajous Trajectory Tracking
Matheus Lohse, Rafael S. Castro, Aurelio T. Salton, Minyue Fu
https://arxiv.org/abs/2404.04973

Nanometer Scanning with Micrometer Sensing: Beating Quantization Constraints in Lissajous Trajectory Tracking
This paper addresses the task of tracking Lissajous trajectories in the presence of quantized positioning sensors. To do so, theoretical results on tracking of continuous time periodic signals in the presence of output quantization are provided. With these results in hand, the application to Lissajous tracking is explored. The method proposed relies on the internal model principle and dispenses perfect knowledge of the system equations. Numerical results show that an arbitrary small scanning re…

This https://arxiv.org/abs/2309.15685 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions
Accurate trajectory prediction is crucial for safe and efficient autonomous driving, but handling partial observations presents significant challenges. To address this, we propose a novel trajectory prediction framework called Partial Observations Prediction (POP) for congested urban road scenarios. The framework consists of two key stages: self-supervised learning (SSL) and feature distillation. POP first employs SLL to help the model learn to reconstruct history representations, and then util…

JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer
Zhihao Guan, Jia-Qi Yang, Yang Yang, Hengshu Zhu, Wenjie Li, Hui Xiong
https://arxiv.org/abs/2404.04313

JobFormer: Skill-Aware Job Recommendation with Semantic-Enhanced Transformer
Job recommendation aims to provide potential talents with suitable job descriptions (JDs) consistent with their career trajectory, which plays an essential role in proactive talent recruitment. In real-world management scenarios, the available JD-user records always consist of JDs, user profiles, and click data, in which the user profiles are typically summarized as the user's skill distribution for privacy reasons. Although existing sophisticated recommendation methods can be directly employed…

Trajectory analysis through entropy characterization over coded representation
Roxana Pe\~na-Mendieta, Ania Mesa-Rodr\'iguez, Ernesto Estevez-Rams, Daniel Estevez-Moya, Danays Kunka
https://arxiv.org/abs/2405.03693

Trajectory analysis through entropy characterization over coded representation
Any continuous curve in a higher dimensional space can be considered a trajectory that can be parameterized by a single variable, usually taken as time. It is well known that a continuous curve can have a fractional dimensionality, which can be estimated using already standard algorithms. However, characterizing a trajectory from an entropic perspective is far less developed. The search for such characterization leads us to use chain coding to discretize the description of a curve. Calculating …

Chauhan Weighted Trajectory Analysis reduces sample size requirements and expedites time-to-efficacy signals in advanced cancer clinical trials
Utkarsh Chauhan, Daylen Mackey, John R. Mackey
https://arxiv.org/abs/2405.02529

Chauhan Weighted Trajectory Analysis reduces sample size requirements and expedites time-to-efficacy signals in advanced cancer clinical trials
As Kaplan-Meier (KM) analysis is limited to single unidirectional endpoints, most advanced cancer randomized clinical trials (RCTs) are powered for either progression free survival (PFS) or overall survival (OS). This discards efficacy information carried by partial responses, complete responses, and stable disease that frequently precede progressive disease and death. Chauhan Weighted Trajectory Analysis (CWTA) is a generalization of KM that simultaneously assesses multiple rank-ordered endpoi…

This https://arxiv.org/abs/2403.10952 has been replaced.
link: https://scholar.google.com/scholar?q=a

Universal Fluctuation-Response Relations of Nonequilibrium Dynamics: A Trajectory Information Geometry Framework
Unraveling the universal principles governing the response of complex systems to environmental changes is crucial for predicting and controlling their behavior. While fluctuation-dissipation relations have been established for systems near equilibrium, a general framework for understanding the responsiveness of systems far from steady states remains elusive. Here, we introduce a novel approach based on the information geometry of stochastic trajectories to derive a set of universal thermodynami…

Fuel-Optimal Trajectory Planning for Lunar Vertical Landing
Kun Wang, Zheng Chen, Jun Li
https://arxiv.org/abs/2404.03926 https://arx…

Fuel-Optimal Trajectory Planning for Lunar Vertical Landing
In this paper, we consider a trajectory planning problem arising from a lunar vertical landing with minimum fuel consumption. The vertical landing requirement is written as a final steering angle constraint, and a nonnegative regularization term is proposed to modify the cost functional. In this way, the final steering angle constraint will be inherently satisfied according to Pontryagin's Minimum Principle. As a result, the modified optimal steering angle has to be determined by solving a tran…

Exploring UAV Networking from the Terrain Information Completeness Perspective: A Tutorial
Zhengying Lou, Ruibo Wang, Baha Eddine Youcef Belmekki, Mustafa A. Kishk, Mohamed-Slim Alouini
https://arxiv.org/abs/2404.04505

Exploring UAV Networking from the Terrain Information Completeness Perspective: A Tutorial
Terrain information is a crucial factor affecting the performance of unmanned aerial vehicle (UAV) networks. As a tutorial, this article provides a unique perspective on the completeness of terrain information, summarizing and enhancing the research on terrain-based UAV deployment. In the presence of complete terrain information, two highly discussed topics are UAV-aided map construction and dynamic trajectory design based on maps. We propose a case study illustrating the mutually reinforcing r…

This https://arxiv.org/abs/2308.04613 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Shallow Encounters' Impact on Asteroid Deflection Prediction and Implications on Trajectory Design
Analytical approximations are commonly employed in the initial trajectory design phase of a mission to rapidly explore a broad design space. In the context of an asteroid deflection mission, accurately predicting deflection is crucial to determining the spacecraft's trajectory that will produce the desired outcome. However, the dynamics involved are intricate, and simplistic models may not fully capture the system's complexity. This study assesses the precision and limitations of analytical mod…

This https://arxiv.org/abs/2401.03281 has been replaced.
initial toot: https://mastoxiv.page/@arXi…

Statistical Response of ENSO Complexity to Initial Condition and Model Parameter Perturbations
Studying the response of a climate system to perturbations has practical significance. Standard methods in computing the trajectory-wise deviation caused by perturbations may suffer from the chaotic nature that makes the model error dominate the true response after a short lead time. Statistical response, which computes the return described by the statistics, provides a systematic way of reaching robust outcomes with an appropriate quantification of the uncertainty and extreme events. In this p…

A scientific paper about the asteroid turned fireball turned meteorite shower - "Atmospheric entry and fragmentation of small asteroid #2024BX1: Bolide trajectory, orbit, dynamics, light curve, and spectrum": https://arxiv.org/abs/2403.00634

This https://arxiv.org/abs/2310.09567 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Automatic and Computationally Efficient Alignment in Fan- and Cone-beam Tomography
This work is concerned with fan- and cone-beam computed tomography with circular source trajectory, where the reconstruction inverse problem requires an accurate knowledge of source, detector and rotational axis relative positions and orientations. We address this additional inverse problem as a preceding step of the reconstruction process directly from the acquired projections. In the cone-beam case, we present a method that estimates both the detector shift (orthogonal to both focal and rotat…

This https://arxiv.org/abs/2311.03655 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

PUMA: Fully Decentralized Uncertainty-aware Multiagent Trajectory Planner with Real-time Image Segmentation-based Frame Alignment
Fully decentralized, multiagent trajectory planners enable complex tasks like search and rescue or package delivery by ensuring safe navigation in unknown environments. However, deconflicting trajectories with other agents and ensuring collision-free paths in a fully decentralized setting is complicated by dynamic elements and localization uncertainty. To this end, this paper presents (1) an uncertainty-aware multiagent trajectory planner and (2) an image segmentation-based frame alignment pipe…

Creating a Trajectory for Code Writing: Algorithmic Reasoning Tasks
Shruthi Ravikumar, Margaret Hamilton, Charles Thevathayan, Maria Spichkova, Kashif Ali, Gayan Wijesinghe
https://arxiv.org/abs/2404.02464

Creating a Trajectory for Code Writing: Algorithmic Reasoning Tasks
Many students in introductory programming courses fare poorly in the code writing tasks of the final summative assessment. Such tasks are designed to assess whether novices have developed the analytical skills to translate from the given problem domain to coding. In the past researchers have used instruments such as code-explain and found that the extent of cognitive depth reached in these tasks correlated well with code writing ability. However, the need for manual marking and personalized int…

Quantum trajectory of the one atom maser
Tristan Benoist, Laurent Bruneau, Cl\'ement Pellegrini
https://arxiv.org/abs/2403.20094 https://

Quantum trajectory of the one atom maser
The evolution of a quantum system undergoing repeated indirect measurements naturally leads to a Markov chain on the set of states which is called a quantum trajectory. In this paper we consider a specific model of such a quantum trajectory associated to the one-atom maser model. It describes the evolution of one mode of the quantized electromagnetic field in a cavity interacting with two-level atoms. When the system is non-resonant we prove that this Markov chain admits a unique invariant prob…

Neural Interaction Energy for Multi-Agent Trajectory Prediction
Kaixin Shen, Ruijie Quan, Linchao Zhu, Jun Xiao, Yi Yang
https://arxiv.org/abs/2404.16579 h…

Neural Interaction Energy for Multi-Agent Trajectory Prediction
Maintaining temporal stability is crucial in multi-agent trajectory prediction. Insufficient regularization to uphold this stability often results in fluctuations in kinematic states, leading to inconsistent predictions and the amplification of errors. In this study, we introduce a framework called Multi-Agent Trajectory prediction via neural interaction Energy (MATE). This framework assesses the interactive motion of agents by employing neural interaction energy, which captures the dynamics of…

This https://arxiv.org/abs/2311.03655 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

PUMA: Fully Decentralized Uncertainty-aware Multiagent Trajectory Planner with Real-time Image Segmentation-based Frame Alignment
Fully decentralized, multiagent trajectory planners enable complex tasks like search and rescue or package delivery by ensuring safe navigation in unknown environments. However, deconflicting trajectories with other agents and ensuring collision-free paths in a fully decentralized setting is complicated by dynamic elements and localization uncertainty. To this end, this paper presents (1) an uncertainty-aware multiagent trajectory planner and (2) an image segmentation-based frame alignment pipe…

Unscented Trajectory Optimization
I. M. Ross, R. J. Proulx, M. Karpenko
https://arxiv.org/abs/2405.02753 https://arxiv.org/pdf/2405.0…

Unscented Trajectory Optimization
In a nutshell, unscented trajectory optimization is the generation of optimal trajectories through the use of an unscented transform. Although unscented trajectory optimization was introduced by the authors about a decade ago, it is reintroduced in this paper as a special instantiation of tychastic optimal control theory. Tychastic optimal control theory (from \textit{Tyche}, the Greek goddess of chance) avoids the use of a Brownian motion and the resulting Itô calculus even though it uses ran…

"If workers are discontented, and their discontent takes reactionary form, then socialists need to have more to say than “stop being so stupid.”
Eyes Left What to make of Lee Anderson’s political trajectory? | Morning Star
https://morningstaronline.co.uk/a…

What to make of Lee Anderson’s political trajectory?
While the Labour-turned-Tory-turned-Reform UK politician might hold deeply objectionable views, there are lessons to be drawn about class discontent and how socialist leadership should respond, argues ANDREW MURRAY

Nikki Haley won her first contest in the Republican presidential nomination race on Sunday after triumphing in Washington, D.C.'s primary.
(It won’t likely change the contest’s trajectory.)

After three days of voting, polls in the Washington race closed at 7 p.m. Eastern on Sunday.
Though only 19 delegates were stake, Haley perhaps had her best chance of defeating Trump in a race where he performed poorly in the last competitive GOP presidential contest in 2016.

Nikki Haley wins D.C. primary, her first victory in GOP nominating race
Unlike Trump, Haley campaigned in Washington ahead of the primary, visiting the city on Friday.

This https://arxiv.org/abs/2302.06794 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepp…

Regge trajectory relations for the universal description of the heavy-light systems: diquarks, mesons, baryons and tetraquarks
Two newly proposed Regge trajectory relations are employed to analyze the heavy-light systems. One of the relations is $M=m_1+m_2+C'+β_x\sqrt{x+c_{0x}}$, $(x=l,\,n_r)$. Another reads $M=m_1+C'+\sqrt{β_x^2(x+c_{0x})+\frac{4}{3}\sqrt{π{β_x}}m^{3/2}_2(x+c_{0x})^{1/4}}$. $M$ is the bound state mass. $m_1$ and $m_2$ are the masses of the heavy constituent and the light constituent, respectively. $l$ is the orbital angular momentum and $n_r$ is the radial quantum number. $β_x$ and $c_{0x}$ are f…

Don't worry everyone, if this Threads federation thing continues on the same trajectory, about 80% of users that will have it on, are already on Fedi.🤣🤣🤣
#Fedi #Fediverse #ActivityPub #Threads #Mastodon #meta

This https://arxiv.org/abs/2402.07232 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

UVTM: Universal Vehicle Trajectory Modeling with ST Feature Domain Generation
Vehicle movement is frequently captured in the form of trajectories, i.e., sequences of timestamped locations. Numerous methods exist that target different tasks involving trajectories such as travel-time estimation, trajectory recovery, and trajectory prediction. However, most methods target only one specific task and cannot be applied universally. Existing efforts to create a universal trajectory model often involve adding prediction modules for adapting to different tasks, while also struggl…

Understanding the effects of spacecraft trajectories through solar coronal mass ejection flux ropes using 3DCOREweb
Hannah Theresa R\"udisser, Andreas Jeffrey Weiss, Justin Le Lou\"edec, Ute V. Amerstorfer, Christian M\"ostl, Emma E. Davies, Helmut Lammer
https://arxiv.org/abs/2405.03271

Understanding the effects of spacecraft trajectories through solar coronal mass ejection flux ropes using 3DCOREweb
This study investigates the impact of spacecraft positioning and trajectory on in situ signatures of coronal mass ejections (CMEs). Employing the 3DCORE model, a 3D flux rope model that can generate in situ profiles for any given point in space and time, we conduct forward modeling to analyze such signatures for various latitudinal and longitudinal positions, with respect to the flux rope apex, at 0.8au. Using this approach, we explore the appearance of the resulting in situ profiles for differ…

#LLMs, even generative LLMs, doesn’t create new things. It just remixes at a phenomenal rate. High-output remixing tends to produce low-quality output, with occasional gems. So on net, they increase the ratio of low-quality garbage to gems. That’s all they can do. And that just gums up the works of, well, everything.
Generative AI, in its current trajectory, cannot be anything but a net-negat…

Nog zo’n doemscenario dat zo hard door de BBBende van vier ontkend zal worden dat het wel wššr moet zijn. https://climatejustice.social/@breadandcircuses/112371624386208660

ATLS: Automated Trailer Loading for Surface Vessels
Amer Abughaida, Meet Gandhi, Jun Heo, Vaishnav Tadiparthi, Yosuke Sakamoto, Joohyun Woo, Sangjae Bae
https://arxiv.org/abs/2405.05426

ATLS: Automated Trailer Loading for Surface Vessels
Automated docking technologies of marine boats have been enlightened by an increasing number of literature. This paper contributes to the literature by proposing a mathematical framework that automates "trailer loading" in the presence of wind disturbances, which is unexplored despite its importance to boat owners. The comprehensive pipeline of localization, system identification, and trajectory optimization is structured, followed by several techniques to improve performance reliability. The p…

BILTS: A novel bi-invariant local trajectory-shape descriptor for rigid-body motion
Arno Verduyn, Erwin Aertbeli\"en, Glenn Maes, Joris De Schutter, Maxim Vochten
https://arxiv.org/abs/2405.04392

BILTS: A novel bi-invariant local trajectory-shape descriptor for rigid-body motion
Measuring the similarity between motions and established motion models is crucial for motion analysis, recognition, generation, and adaptation. To enhance similarity measurement across diverse contexts, invariant motion descriptors have been proposed. However, for rigid-body motion, few invariant descriptors exist that are bi-invariant, meaning invariant to both the body and world reference frames used to describe the motion. Moreover, their robustness to singularities is limited. This paper in…

On a precessing jet-nozzle scenario with a common helical trajectory-pattern for blazar 3C345
S. J. Qian
https://arxiv.org/abs/2402.15157 https://

On a precessing jet-nozzle scenario with a common helical trajectory-pattern for blazar 3C345
The kinematics and flux evolution of the superluminal knots in blazar 3C345 were interpreted in the framework of the precessing jet-nozzle scenario with a precessing common helical trajectory-pattern. We show that the jet in 3C345 precesses with a period of 7.3yr and the superluminal knots move consistently along a precessing common helical trajectory-pattern in their inner jet regions, which can extend to core distances of about 1.2mas or traveled distances of about 300pc (for knots C4 and C9)…

AoI-aware Sensing Scheduling and Trajectory Optimization for Multi-UAV-assisted Wireless Backscatter Networks
Yusi Long (Sherman), Songhan Zhao (Sherman), Shimin Gong (Sherman), Bo Gu (Sherman), Dusit Niyato (Sherman), Xuemin (Sherman), Shen
https://arxiv.org/abs/2404.19449

AoI-aware Sensing Scheduling and Trajectory Optimization for Multi-UAV-assisted Wireless Backscatter Networks
This paper considers multiple unmanned aerial vehicles (UAVs) to assist sensing data transmissions from the ground users (GUs) to a remote base station (BS). Each UAV collects sensing data from the GUs and then forwards the sensing data to the remote BS. The GUs first backscatter their data to the UAVs and then all UAVs forward data to the BS by the nonorthogonal multiple access (NOMA) transmissions. We formulate a multi-stage stochastic optimization problem to minimize the long-term time-avera…

An Optimal Control Problem Arising in Sailboat Trajectory Optimization
Carlo Ciccarella, Robert C. Dalang, Laura Vinckenbosch
https://arxiv.org/abs/2404.03773

An Optimal Control Problem Arising in Sailboat Trajectory Optimization
We formulate the optimal control problem for a sailboat that seeks to reach an upwind buoy, under the hypothesis that the wind speed is constant and the wind direction is a Brownian motion. The yacht moves in $\mathbb{R}^2$ and the target is a ball of radius $η\geq 0$ centered at the origin. The yacht can be subject to a tacking cost $c\geq 0$. We consider three control problems depending on the value of the parameters: the first one corresponds to the case $η=0$, $c=0$, the second to the cas…

Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography
Lei Xie, Qingrun Zeng, Huajun Zhou, Guoqiang Xie, Mingchu Li, Jiahao Huang, Jianan Cui, Hao Chen, Yuanjing Feng
https://arxiv.org/abs/2402.18856

Anatomy-guided fiber trajectory distribution estimation for cranial nerves tractography
Diffusion MRI tractography is an important tool for identifying and analyzing the intracranial course of cranial nerves (CNs). However, the complex environment of the skull base leads to ambiguous spatial correspondence between diffusion directions and fiber geometry, and existing diffusion tractography methods of CNs identification are prone to producing erroneous trajectories and missing true positive connections. To overcome the above challenge, we propose a novel CNs identification framewor…

Quantum trajectory of the one atom maser
Tristan Benoist, Laurent Bruneau, Cl\'ement Pellegrini
https://arxiv.org/abs/2403.20094 https://

Quantum trajectory of the one atom maser
The evolution of a quantum system undergoing repeated indirect measurements naturally leads to a Markov chain on the set of states which is called a quantum trajectory. In this paper we consider a specific model of such a quantum trajectory associated to the one-atom maser model. It describes the evolution of one mode of the quantized electromagnetic field in a cavity interacting with two-level atoms. When the system is non-resonant we prove that this Markov chain admits a unique invariant prob…

Characterized Diffusion and Spatial-Temporal Interaction Network for Trajectory Prediction in Autonomous Driving
Haicheng Liao, Xuelin Li, Yongkang Li, Hanlin Kong, Chengyue Wang, Bonan Wang, Yanchen Guan, KaHou Tam, Zhenning Li, Chengzhong Xu
https://arxiv.org/abs/2405.02145

Characterized Diffusion and Spatial-Temporal Interaction Network for Trajectory Prediction in Autonomous Driving
Trajectory prediction is a cornerstone in autonomous driving (AD), playing a critical role in enabling vehicles to navigate safely and efficiently in dynamic environments. To address this task, this paper presents a novel trajectory prediction model tailored for accuracy in the face of heterogeneous and uncertain traffic scenarios. At the heart of this model lies the Characterized Diffusion Module, an innovative module designed to simulate traffic scenarios with inherent uncertainty. This modul…

This https://arxiv.org/abs/2402.04154 has been replaced.
link: https://scholar.google.com/scholar?q=a

Read to Play (R2-Play): Decision Transformer with Multimodal Game Instruction
Developing a generalist agent is a longstanding objective in artificial intelligence. Previous efforts utilizing extensive offline datasets from various tasks demonstrate remarkable performance in multitasking scenarios within Reinforcement Learning. However, these works encounter challenges in extending their capabilities to new tasks. Recent approaches integrate textual guidance or visual trajectory into decision networks to provide task-specific contextual cues, representing a promising dire…

A Two Time-Scale Joint Optimization Approach for UAV-assisted MEC
Zemin Sun, Geng Sun, Long He, Fang Mei, Shuang Liang, Yanheng Liu
https://arxiv.org/abs/2404.04597

A Two Time-Scale Joint Optimization Approach for UAV-assisted MEC
Unmanned aerial vehicles (UAV)-assisted mobile edge computing (MEC) is emerging as a promising paradigm to provide aerial-terrestrial computing services close to mobile devices (MDs). However, meeting the demands of computation-intensive and delay-sensitive tasks for MDs poses several challenges, including the demand-supply contradiction between MDs and MEC servers, the demand-supply heterogeneity between MDs and MEC servers, the trajectory control requirements on energy efficiency and timeline…

This https://arxiv.org/abs/2403.19324 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Rapid nonlinear convex guidance using a monomial method
This paper introduces a framework by which the nonlinear trajectory optimization problem is posed as a path-planning problem in a space liberated of dynamics. In this space, general state constraints for continuous and impulsive control problems are encoded as linear constraints on the native overparameterized variables. This framework is enabled by nonlinear expansion in the vicinity of a reference in terms of fundamental solutions and a minimal nonlinear basis of mixed monomials in problem in…

Trajectory Consistency Distillation
Jianbin Zheng, Minghui Hu, Zhongyi Fan, Chaoyue Wang, Changxing Ding, Dacheng Tao, Tat-Jen Cham
https://arxiv.org/abs/2402.19159

Trajectory Consistency Distillation
Latent Consistency Model (LCM) extends the Consistency Model to the latent space and leverages the guided consistency distillation technique to achieve impressive performance in accelerating text-to-image synthesis. However, we observed that LCM struggles to generate images with both clarity and detailed intricacy. To address this limitation, we initially delve into and elucidate the underlying causes. Our investigation identifies that the primary issue stems from errors in three distinct areas…

This https://arxiv.org/abs/2403.19324 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_mat…

Rapid nonlinear convex guidance using a monomial method
This paper introduces a framework by which the nonlinear trajectory optimization problem is posed as a path-planning problem in a space liberated of dynamics. In this space, general state constraints for continuous and impulsive control problems are encoded as linear constraints on the native overparameterized variables. This framework is enabled by nonlinear expansion in the vicinity of a reference in terms of fundamental solutions and a minimal nonlinear basis of mixed monomials in problem in…

Learning Stochastic Dynamics from Data
Ziheng Guo, Igor Cialenco, Ming Zhong
https://arxiv.org/abs/2403.02595 https://arxiv.org/pdf/2…

Learning Stochastic Dynamics from Data
We present a noise guided trajectory based system identification method for inferring the dynamical structure from observation generated by stochastic differential equations. Our method can handle various kinds of noise, including the case when the the components of the noise is correlated. Our method can also learn both the noise level and drift term together from trajectory. We present various numerical tests for showcasing the superior performance of our learning algorithm.

This https://arxiv.org/abs/2208.12006 has been replaced.
link: https://scholar.google.com/scholar?q=a

Lie Algebraic Quantum Phase Reduction
We introduce a general framework of phase reduction theory for quantum nonlinear oscillators. By employing the quantum trajectory theory, we define the limit-cycle trajectory and the phase according to a stochastic Schrödinger equation. Because a perturbation is represented by unitary transformation in quantum dynamics, we calculate phase response curves with respect to generators of a Lie algebra. Our method shows that the continuous measurement yields phase clusters and alters the phase resp…

This https://arxiv.org/abs/2404.12079 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Trajectory Planning for Autonomous Vehicle Using Iterative Reward Prediction in Reinforcement Learning
Traditional trajectory planning methods for autonomous vehicles have several limitations. Heuristic and explicit simple rules make trajectory lack generality and complex motion. One of the approaches to resolve the above limitations of traditional trajectory planning methods is trajectory planning using reinforcement learning. However, reinforcement learning suffers from instability of learning and prior works of trajectory planning using reinforcement learning didn't consider the uncertainties…

Bi-level Trajectory Optimization on Uneven Terrains with Differentiable Wheel-Terrain Interaction Model
Amith Manoharan, Aditya Sharma, Himani Belsare, Kaustab Pal, K. Madhava Krishna, Arun Kumar Singh
https://arxiv.org/abs/2404.03307

Bi-level Trajectory Optimization on Uneven Terrains with Differentiable Wheel-Terrain Interaction Model
Navigation of wheeled vehicles on uneven terrain necessitates going beyond the 2D approaches for trajectory planning. Specifically, it is essential to incorporate the full 6dof variation of vehicle pose and its associated stability cost in the planning process. To this end, most recent works aim to learn a neural network model to predict the vehicle evolution. However, such approaches are data-intensive and fraught with generalization issues. In this paper, we present a purely model-based appro…

Bi-level Trajectory Optimization on Uneven Terrains with Differentiable Wheel-Terrain Interaction Model
Amith Manoharan, Aditya Sharma, Himani Belsare, Kaustab Pal, K. Madhava Krishna, Arun Kumar Singh
https://arxiv.org/abs/2404.03307

Bi-level Trajectory Optimization on Uneven Terrains with Differentiable Wheel-Terrain Interaction Model
Navigation of wheeled vehicles on uneven terrain necessitates going beyond the 2D approaches for trajectory planning. Specifically, it is essential to incorporate the full 6dof variation of vehicle pose and its associated stability cost in the planning process. To this end, most recent works aim to learn a neural network model to predict the vehicle evolution. However, such approaches are data-intensive and fraught with generalization issues. In this paper, we present a purely model-based appro…

This https://arxiv.org/abs/2011.12639 has been replaced.
link: https://scholar.google.com/scholar?q=a

Computation of Feedback Control Laws Based on Switched Tracking of Demonstrations
A common approach in robotics is to learn tasks by generalizing from special cases given by a so-called demonstrator. In this paper, we apply this paradigm and present an algorithm that uses a demonstrator (typically given by a trajectory optimizer) to automatically synthesize feedback controllers for steering a system described by ordinary differential equations into a goal set. The resulting feedback control law switches between the demonstrations that it uses as reference trajectories. In co…

LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models
Mingxing Peng (Frank), Xusen Guo (Frank), Xianda Chen (Frank), Meixin Zhu (Frank), Kehua Chen (Frank), Hao (Frank), Yang, Xuesong Wang, Yinhai Wang
https://arxiv.org/abs/2403.18344

LC-LLM: Explainable Lane-Change Intention and Trajectory Predictions with Large Language Models
To ensure safe driving in dynamic environments, autonomous vehicles should possess the capability to accurately predict the lane change intentions of surrounding vehicles in advance and forecast their future trajectories. Existing motion prediction approaches have ample room for improvement, particularly in terms of long-term prediction accuracy and interpretability. In this paper, we address these challenges by proposing LC-LLM, an explainable lane change prediction model that leverages the st…

This https://arxiv.org/abs/2309.12251 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Planning Optimal Trajectories for Mobile Manipulators under End-effector Trajectory Continuity Constraint
Mobile manipulators have been employed in many applications that are traditionally performed by either multiple fixed-base robots or a large robotic system. This capability is enabled by the mobility of the mobile base. However, the mobile base also brings redundancy to the system, which makes mobile manipulator motion planning more challenging. In this paper, we tackle the mobile manipulator motion planning problem under the end-effector trajectory continuity constraint in which the end-effect…

MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving
Haicheng Liao, Zhenning Li, Chengyue Wang, Huanming Shen, Bonan Wang, Dongping Liao, Guofa Li, Chengzhong Xu
https://arxiv.org/abs/2405.01266

MFTraj: Map-Free, Behavior-Driven Trajectory Prediction for Autonomous Driving
This paper introduces a trajectory prediction model tailored for autonomous driving, focusing on capturing complex interactions in dynamic traffic scenarios without reliance on high-definition maps. The model, termed MFTraj, harnesses historical trajectory data combined with a novel dynamic geometric graph-based behavior-aware module. At its core, an adaptive structure-aware interactive graph convolutional network captures both positional and behavioral features of road users, preserving spatia…

This https://arxiv.org/abs/2404.12079 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Trajectory Planning for Autonomous Vehicle Using Iterative Reward Prediction in Reinforcement Learning
Traditional trajectory planning methods for autonomous vehicles have several limitations. For example, heuristic and explicit simple rules limit generalizability and hinder complex motions. These limitations can be addressed using reinforcement learning-based trajectory planning. However, reinforcement learning suffers from unstable learning and existing reinforcement learning-based trajectory planning methods do not consider the uncertainties. Thus, this paper, proposes a reinforcement learnin…

Low-cost adaptive obstacle avoidance trajectory control for express delivery drone
Yanhui Zhang, Caisheng Wei, Yifan Zhang, Congcong Tian, Weifang Chen
https://arxiv.org/abs/2403.19956

Low-cost adaptive obstacle avoidance trajectory control for express delivery drone
This paper studies quadcopters obstacle avoidance trajectory control (OATC) problem for express delivery. A new nonlinear adaptive learning controller that is low-cost and portable to different wheelbase sizes is proposed to adapt to large-angle maneuvers and load changes in UAV delivery missions. The controller consists of a nonlinear variable gain (NLVG) function and an extreme value search (ES) algorithm to reduce overshoot and settling time. Finally, simulations were conducted on a quadcopt…

Long-Term Human Trajectory Prediction using 3D Dynamic Scene Graphs
Nicolas Gorlo, Lukas Schmid, Luca Carlone
https://arxiv.org/abs/2405.00552 https://

Long-Term Human Trajectory Prediction using 3D Dynamic Scene Graphs
We present a novel approach for long-term human trajectory prediction, which is essential for long-horizon robot planning in human-populated environments. State-of-the-art human trajectory prediction methods are limited by their focus on collision avoidance and short-term planning, and their inability to model complex interactions of humans with the environment. In contrast, our approach overcomes these limitations by predicting sequences of human interactions with the environment and using thi…

Optimization of 3-D flight trajectory of variable trim kites for airborne wind energy production
Rafal Noga, Xaver Paulig, Lukas Schmidt, Benjamin Karg, Manfred Quack, Mahmoud Soliman
https://arxiv.org/abs/2403.00382

Optimization of 3-D flight trajectory of variable trim kites for airborne wind energy production
Skysails Power GmbH is the leading manufacturer of light and efficient power kites that harness the wind's untapped supplies at high altitudes, aiming at profoundly altering wind energy's impact in achieving the global energy transition. Novel, variable trim kites have been developed that allow to modulate the aerodynamic coefficients of the airborne system, significantly improving the overall system efficiency. The flight control of variable trim kites is much more complex than that of previou…

Implicit Swept Volume SDF: Enabling Continuous Collision-Free Trajectory Generation for Arbitrary Shapes
Jingping Wang, Tingrui Zhang, Qixuan Zhang, Chuxiao Zeng, Jingyi Yu, Chao Xu, Lan Xu, Fei Gao
https://arxiv.org/abs/2405.00362

Implicit Swept Volume SDF: Enabling Continuous Collision-Free Trajectory Generation for Arbitrary Shapes
In the field of trajectory generation for objects, ensuring continuous collision-free motion remains a huge challenge, especially for non-convex geometries and complex environments. Previous methods either oversimplify object shapes, which results in a sacrifice of feasible space or rely on discrete sampling, which suffers from the "tunnel effect". To address these limitations, we propose a novel hierarchical trajectory generation pipeline, which utilizes the Swept Volume Signed Distance Field …

Generative Planning with Fast Collision Checks for High Speed Navigation
Craig Knuth, Cora Dimmig, Brian Bittner
https://arxiv.org/abs/2405.04498 https://<…

Generative Planning with Fast Collision Checks for High Speed Navigation
Reasoning about large numbers of diverse plans to achieve high speed navigation in cluttered environments remains a challenge for robotic systems even in the case of perfect perceptual information. Often, this is tackled by methods that iteratively optimize around a prior seeded trajectory and consequently restrict to local optima. We present a novel planning method using normalizing flows (NFs) to encode expert-styled motion primitives. We also present an accelerated collision checking framewo…

Structured Deep Neural Networks-Based Backstepping Trajectory Tracking Control for Lagrangian Systems
Jiajun Qian, Liang Xu, Xiaoqiang Ren, Xiaofan Wang
https://arxiv.org/abs/2403.00381

Structured Deep Neural Networks-Based Backstepping Trajectory Tracking Control for Lagrangian Systems
Deep neural networks (DNN) are increasingly being used to learn controllers due to their excellent approximation capabilities. However, their black-box nature poses significant challenges to closed-loop stability guarantees and performance analysis. In this paper, we introduce a structured DNN-based controller for the trajectory tracking control of Lagrangian systems using backing techniques. By properly designing neural network structures, the proposed controller can ensure closed-loop stabili…

This https://arxiv.org/abs/2403.17448 has been replaced.
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Adaptive Line-Of-Sight guidance law based on vector fields path following for underactuated unmanned surface vehicle
The focus of this paper is to develop a methodology that enables an unmanned surface vehicle (USV) to efficiently track a planned path. The introduction of a vector field-based adaptive line-of-sight guidance law (VFALOS) for accurate trajectory tracking and minimizing the overshoot response time during USV tracking of curved paths improves the overall line-of-sight (LOS) guidance method. These improvements contribute to faster convergence to the desired path, reduce oscillations, and can mitig…

A Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving Cars
Cristian Tiriolo, Walter Lucia
https://arxiv.org/abs/2405.01753

A Feedback Linearized Model Predictive Control Strategy for Input-Constrained Self-Driving Cars
This paper proposes a novel real-time affordable solution to the trajectory tracking control problem for self-driving cars subject to longitudinal and steering angular velocity constraints. To this end, we develop a dual-mode Model Predictive Control (MPC) solution starting from an input-output feedback linearized description of the vehicle kinematics. First, we derive the state-dependent input constraints acting on the linearized model and characterize their worst-case time-invariant inner app…

Behavioral Manifolds: Representing the Landscape of Grasp Affordances in the Relative Pose Space
Michael Zechmair, Yannick Morel
https://arxiv.org/abs/2405.04188

Behavioral Manifolds: Representing the Landscape of Grasp Affordances in the Relative Pose Space
The use of machine learning to investigate grasp affordances has received extensive attention over the past several decades. The existing literature provides a robust basis to build upon, though a number of aspects may be improved. Results commonly work in terms of grasp configuration, with little consideration for the manner in which the grasp may be (re-)produced from a reachability and trajectory planning perspective. In addition, the majority of existing learning approaches focus of produci…

Bidirectional Human Interactive AI Framework for Social Robot Navigation
Tuba Girgin, Emre Girgin, Yigit Yildirim, Emre Ugur, Mehmet Haklidir
https://arxiv.org/abs/2404.04069

Bidirectional Human Interactive AI Framework for Social Robot Navigation
Trustworthiness is a crucial concept in the context of human-robot interaction. Cooperative robots must be transparent regarding their decision-making process, especially when operating in a human-oriented environment. This paper presents a comprehensive end-to-end framework aimed at fostering trustworthy bidirectional human-robot interaction in collaborative environments for the social navigation of mobile robots. Our method enables a mobile robot to predict the trajectory of people and adjust…

Bidirectional Human Interactive AI Framework for Social Robot Navigation
Tuba Girgin, Emre Girgin, Yigit Yildirim, Emre Ugur, Mehmet Haklidir
https://arxiv.org/abs/2404.04069

Bidirectional Human Interactive AI Framework for Social Robot Navigation
Trustworthiness is a crucial concept in the context of human-robot interaction. Cooperative robots must be transparent regarding their decision-making process, especially when operating in a human-oriented environment. This paper presents a comprehensive end-to-end framework aimed at fostering trustworthy bidirectional human-robot interaction in collaborative environments for the social navigation of mobile robots. Our method enables a mobile robot to predict the trajectory of people and adjust…

Interaction-Aware Vehicle Motion Planning with Collision Avoidance Constraints in Highway Traffic
Dongryul Kim, Hyeonjeong Kim, Kyoungseok Han
https://arxiv.org/abs/2404.01661

Interaction-Aware Vehicle Motion Planning with Collision Avoidance Constraints in Highway Traffic
This paper proposes collision-free optimal trajectory planning for autonomous vehicles in highway traffic, where vehicles need to deal with the interaction among each other. To address this issue, a novel optimal control framework is suggested, which couples the trajectory of surrounding vehicles with collision avoidance constraints. Additionally, we describe a trajectory optimization technique under state constraints, utilizing a planner based on Pontryagin's Minimum Principle, capable of nume…

This https://arxiv.org/abs/2309.06837 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Time-Optimal Gate-Traversing Planner for Autonomous Drone Racing
In drone racing, the time-minimum trajectory is affected by the drone's capabilities, the layout of the race track, and the configurations of the gates (e.g., their shapes and sizes). However, previous studies neglect the configuration of the gates, simply rendering drone racing a waypoint-passing task. This formulation often leads to a conservative choice of paths through the gates, as the spatial potential of the gates is not fully utilized. To address this issue, we present a time-optimal pl…

Enhancing Robotic Adaptability: Integrating Unsupervised Trajectory Segmentation and Conditional ProMPs for Dynamic Learning Environments
Tianci Gao
https://arxiv.org/abs/2404.19412

Enhancing Robotic Adaptability: Integrating Unsupervised Trajectory Segmentation and Conditional ProMPs for Dynamic Learning Environments
We propose a novel framework for enhancing robotic adaptability and learning efficiency, which integrates unsupervised trajectory segmentation with adaptive probabilistic movement primitives (ProMPs). By employing a cutting-edge deep learning architecture that combines autoencoders and Recurrent Neural Networks (RNNs), our approach autonomously pinpoints critical transitional points in continuous, unlabeled motion data, thus significantly reducing dependence on extensively labeled datasets. Thi…

Trajectory Planning for Autonomous Vehicle Using Iterative Reward Prediction in Reinforcement Learning
Hyunwoo Park
https://arxiv.org/abs/2404.12079 https:…

Trajectory Planning for Autonomous Vehicle Using Iterative Reward Prediction in Reinforcement Learning
Traditional trajectory planning methods for autonomous vehicles have several limitations. Heuristic and explicit simple rules make trajectory lack generality and complex motion. One of the approaches to resolve the above limitations of traditional trajectory planning methods is trajectory planning using reinforcement learning. However, reinforcement learning suffers from instability of learning and prior works of trajectory planning using reinforcement learning didn't consider the uncertainties…

Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer ($\Pi$-MPC)
Luis Pabon, Johannes K\"ohler, John Irvin Alora, Patrick Benito Eberhard, Andrea Carron, Melanie N. Zeilinger, Marco Pavone
https://arxiv.org/abs/2404.01550

Perfecting Periodic Trajectory Tracking: Model Predictive Control with a Periodic Observer ($Π$-MPC)
In Model Predictive Control (MPC), discrepancies between the actual system and the predictive model can lead to substantial tracking errors and significantly degrade performance and reliability. While such discrepancies can be alleviated with more complex models, this often complicates controller design and implementation. By leveraging the fact that many trajectories of interest are periodic, we show that perfect tracking is possible when incorporating a simple observer that estimates and comp…

Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay
Mahya Ramezani, Jose Luis Sanchez-Lopez
https://arxiv.org/abs/2402.18487

Human-Centric Aware UAV Trajectory Planning in Search and Rescue Missions Employing Multi-Objective Reinforcement Learning with AHP and Similarity-Based Experience Replay
The integration of Unmanned Aerial Vehicles (UAVs) into Search and Rescue (SAR) missions presents a promising avenue for enhancing operational efficiency and effectiveness. However, the success of these missions is not solely dependent on the technical capabilities of the drones but also on their acceptance and interaction with humans on the ground. This paper explores the effect of human-centric factor in UAV trajectory planning for SAR missions. We introduce a novel approach based on the rein…

A Preprocessing and Evaluation Toolbox for Trajectory Prediction Research on the Drone Datasets
Theodor Westny, Bj\"orn Olofsson, Erik Frisk
https://arxiv.org/abs/2405.00604 …

A Preprocessing and Evaluation Toolbox for Trajectory Prediction Research on the Drone Datasets
The availability of high-quality datasets is crucial for the development of behavior prediction algorithms in autonomous vehicles. This paper highlights the need for standardizing the use of certain datasets for motion forecasting research to simplify comparative analysis and proposes a set of tools and practices to achieve this. Drawing on extensive experience and a comprehensive review of current literature, we summarize our proposals for preprocessing, visualizing, and evaluation in the form…

This https://arxiv.org/abs/2306.06192 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Ada-NAV: Adaptive Trajectory Length-Based Sample Efficient Policy Learning for Robotic Navigation
Trajectory length stands as a crucial hyperparameter within reinforcement learning (RL) algorithms, significantly contributing to the sample inefficiency in robotics applications. Motivated by the pivotal role trajectory length plays in the training process, we introduce Ada-NAV, a novel adaptive trajectory length scheme designed to enhance the training sample efficiency of RL algorithms in robotic navigation tasks. Unlike traditional approaches that treat trajectory length as a fixed hyperpara…

This https://arxiv.org/abs/2402.01397 has been replaced.
link: https://scholar.google.com/scholar?q=a

A survey on robustness in trajectory prediction for autonomous vehicles
Autonomous vehicles rely on accurate trajectory prediction to inform decision-making processes related to navigation and collision avoidance. However, current trajectory prediction models show signs of overfitting, which may lead to unsafe or suboptimal behavior. To address these challenges, this paper presents a comprehensive framework that categorizes and assesses the definitions and strategies used in the literature on evaluating and improving the robustness of trajectory prediction models. …

This https://arxiv.org/abs/2402.15604 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Goal-Reaching Trajectory Design Near Danger with Piecewise Affine Reach-avoid Computation
Autonomous mobile robots must maintain safety, but should not sacrifice performance, leading to the classical reach-avoid problem. This paper seeks to compute trajectory plans for which a robot is guaranteed to reach a goal and to avoid obstacles in the specific near-danger case, also known as a narrow gap, where the agent starts near the goal, but must navigate through tight obstacles that block its path. The proposed method builds off of a common approach of using a simplified planning model …

Reachability-based Trajectory Design via Exact Formulation of Implicit Neural Signed Distance Functions
Jonathan Michaux, Qingyi Chen, Challen Enninful Adu, Jinsun Liu, Ram Vasudevan
https://arxiv.org/abs/2403.12280

Reachability-based Trajectory Design via Exact Formulation of Implicit Neural Signed Distance Functions
Generating receding-horizon motion trajectories for autonomous vehicles in real-time while also providing safety guarantees is challenging. This is because a future trajectory needs to be planned before the previously computed trajectory is completely executed. This becomes even more difficult if the trajectory is required to satisfy continuous-time collision-avoidance constraints while accounting for a large number of obstacles. To address these challenges, this paper proposes a novel real-tim…

This https://arxiv.org/abs/2306.06192 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Ada-NAV: Adaptive Trajectory Length-Based Sample Efficient Policy Learning for Robotic Navigation
Trajectory length stands as a crucial hyperparameter within reinforcement learning (RL) algorithms, significantly contributing to the sample inefficiency in robotics applications. Motivated by the pivotal role trajectory length plays in the training process, we introduce Ada-NAV, a novel adaptive trajectory length scheme designed to enhance the training sample efficiency of RL algorithms in robotic navigation tasks. Unlike traditional approaches that treat trajectory length as a fixed hyperpara…

Trajectory Planning using Reinforcement Learning for Interactive Overtaking Maneuvers in Autonomous Racing Scenarios
Levent \"Ogretmen, Mo Chen, Phillip Pitschi, Boris Lohmann
https://arxiv.org/abs/2404.10658

Trajectory Planning using Reinforcement Learning for Interactive Overtaking Maneuvers in Autonomous Racing Scenarios
Conventional trajectory planning approaches for autonomous racing are based on the sequential execution of prediction of the opposing vehicles and subsequent trajectory planning for the ego vehicle. If the opposing vehicles do not react to the ego vehicle, they can be predicted accurately. However, if there is interaction between the vehicles, the prediction loses its validity. For high interaction, instead of a planning approach that reacts exclusively to the fixed prediction, a trajectory pla…

This https://arxiv.org/abs/2310.00156 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning Generalizable Tool-use Skills through Trajectory Generation
Autonomous systems that efficiently utilize tools can assist humans in completing many common tasks such as cooking and cleaning. However, current systems fall short of matching human-level of intelligence in terms of adapting to novel tools. Prior works based on affordance often make strong assumptions about the environments and cannot scale to more complex, contact-rich tasks. In this work, we tackle this challenge and explore how agents can learn to use previously unseen tools to manipulate …

This https://arxiv.org/abs/2310.00156 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Learning Generalizable Tool-use Skills through Trajectory Generation
Autonomous systems that efficiently utilize tools can assist humans in completing many common tasks such as cooking and cleaning. However, current systems fall short of matching human-level of intelligence in terms of adapting to novel tools. Prior works based on affordance often make strong assumptions about the environments and cannot scale to more complex, contact-rich tasks. In this work, we tackle this challenge and explore how agents can learn to use previously unseen tools to manipulate …

Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps
Timothy Chen, Ola Shorinwa, Weijia Zeng, Joseph Bruno, Philip Dames, Mac Schwager
https://arxiv.org/abs/2403.02751

Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps
We present Splat-Nav, a navigation pipeline that consists of a real-time safe planning module and a robust state estimation module designed to operate in the Gaussian Splatting (GSplat) environment representation, a popular emerging 3D scene representation from computer vision. We formulate rigorous collision constraints that can be computed quickly to build a guaranteed-safe polytope corridor through the map. We then optimize a B-spline trajectory through this corridor. We also develop a real-…

This https://arxiv.org/abs/2308.01557 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Motion Planning Diffusion: Learning and Planning of Robot Motions with Diffusion Models
Learning priors on trajectory distributions can help accelerate robot motion planning optimization. Given previously successful plans, learning trajectory generative models as priors for a new planning problem is highly desirable. Prior works propose several ways on utilizing this prior to bootstrapping the motion planning problem. Either sampling the prior for initializations or using the prior distribution in a maximum-a-posterior formulation for trajectory optimization. In this work, we prop…

This https://arxiv.org/abs/2403.09900 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

DTG : Diffusion-based Trajectory Generation for Mapless Global Navigation
We present a novel end-to-end diffusion-based trajectory generation method, DTG, for mapless global navigation in challenging outdoor scenarios with occlusions and unstructured off-road features like grass, buildings, bushes, etc. Given a distant goal, our approach computes a trajectory that satisfies the following goals: (1) minimize the travel distance to the goal; (2) maximize the traversability by choosing paths that do not lie in undesirable areas. Specifically, we present a novel Conditio…

This https://arxiv.org/abs/2403.18643 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Sampling-Based Motion Planning with Online Racing Line Generation for Autonomous Driving on Three-Dimensional Race Tracks
Existing approaches to trajectory planning for autonomous racing employ sampling-based methods, generating numerous jerk-optimal trajectories and selecting the most favorable feasible trajectory based on a cost function penalizing deviations from an offline-calculated racing line. While successful on oval tracks, these methods face limitations on complex circuits due to the simplistic geometry of jerk-optimal edges failing to capture the complexity of the racing line. Additionally, they only co…

DITTO: Demonstration Imitation by Trajectory Transformation
Nick Heppert, Max Argus, Tim Welschehold, Thomas Brox, Abhinav Valada
https://arxiv.org/abs/2403.15203

DITTO: Demonstration Imitation by Trajectory Transformation
Teaching robots new skills quickly and conveniently is crucial for the broader adoption of robotic systems. In this work, we address the problem of one-shot imitation from a single human demonstration, given by an RGB-D video recording through a two-stage process. In the first stage which is offline, we extract the trajectory of the demonstration. This entails segmenting manipulated objects and determining their relative motion in relation to secondary objects such as containers. Subsequently, …

Factored Task and Motion Planning with Combined Optimization, Sampling and Learning
Joaquim Ortiz-Haro
https://arxiv.org/abs/2404.03567 https://

Factored Task and Motion Planning with Combined Optimization, Sampling and Learning
In this thesis, we aim to improve the performance of TAMP algorithms from three complementary perspectives. First, we investigate the integration of discrete task planning with continuous trajectory optimization. Our main contribution is a conflict-based solver that automatically discovers why a task plan might fail when considering the constraints of the physical world. This information is then fed back into the task planner, resulting in an efficient, bidirectional, and intuitive interface be…

Factored Task and Motion Planning with Combined Optimization, Sampling and Learning
Joaquim Ortiz-Haro
https://arxiv.org/abs/2404.03567 https://

Factored Task and Motion Planning with Combined Optimization, Sampling and Learning
In this thesis, we aim to improve the performance of TAMP algorithms from three complementary perspectives. First, we investigate the integration of discrete task planning with continuous trajectory optimization. Our main contribution is a conflict-based solver that automatically discovers why a task plan might fail when considering the constraints of the physical world. This information is then fed back into the task planner, resulting in an efficient, bidirectional, and intuitive interface be…

This https://arxiv.org/abs/2310.19620 has been replaced.
link: https://scholar.google.com/scholar?q=a

Large Trajectory Models are Scalable Motion Predictors and Planners
Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model…

D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation
Jun Yamada, Shaohong Zhong, Jack Collins, Ingmar Posner
https://arxiv.org/abs/2403.12861

D-Cubed: Latent Diffusion Trajectory Optimisation for Dexterous Deformable Manipulation
Mastering dexterous robotic manipulation of deformable objects is vital for overcoming the limitations of parallel grippers in real-world applications. Current trajectory optimisation approaches often struggle to solve such tasks due to the large search space and the limited task information available from a cost function. In this work, we propose D-Cubed, a novel trajectory optimisation method using a latent diffusion model (LDM) trained from a task-agnostic play dataset to solve dexterous def…

Pioneering SE(2)-Equivariant Trajectory Planning for Automated Driving
Steffen Hagedorn, Marcel Milich, Alexandru P. Condurache
https://arxiv.org/abs/2403.11304

Pioneering SE(2)-Equivariant Trajectory Planning for Automated Driving
Planning the trajectory of the controlled ego vehicle is a key challenge in automated driving. As for human drivers, predicting the motions of surrounding vehicles is important to plan the own actions. Recent motion prediction methods utilize equivariant neural networks to exploit geometric symmetries in the scene. However, no existing method combines motion prediction and trajectory planning in a joint step while guaranteeing equivariance under roto-translations of the input space. We address …