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新论文 · v1arXiv:2610.36407

A 2-DOF MultiBody Track Joint for Race Car Optimization

Srikanth Sivaramakrishnan, Behzad Danaei, Adwait Verulkar, Ryan Trinter

原文摘要

TrackJoint is a Modelica MultiBody component that turns a three-dimensional road surface into a joint with two degrees of freedom. The two joint coordinates are the distance travelled along a reference line and the sideways offset from that line. Together they give the position, orientation and angular velocity of a single frame_b connector. All road geometry reaches the joint as a small set of periodic piecewise-quintic splines, so one component can represent any circuit whose splines have been fitted beforehand. The road constraint is closed with d'Alembert's principle. The two generalized forces that belong to the joint coordinates are set to zero, so no Lagrange multipliers are needed. The joint is connected to a multibody race car in Dymola and the pair is exported as one FMI~3.0 FMU. The same FMU is then used for time-domain simulation and for CasADi/IPOPT minimum lap-time optimization, with no change to the model in between. This works because the joint keeps the arc-length coordinate as a real state and publishes its time derivative, which is the quantity that lets the problem be rewritten with distance We report results for minimum lap time simulations on the Singapore Formula~1 circuit.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.14087

Gradient-Free Neural Hamilton-Jacobi Reachability for Scalable Safety-Critical Control

Zeyuan Feng, Ali Fuat Sahin, Santiago Thorup, Somil Bansal

原文摘要

Hamilton-Jacobi (HJ) reachability provides a principled framework for synthesizing safety certificates and robust controllers for safety-critical robotic systems. However, applying reachability analysis to high-dimensional nonlinear systems remains challenging: classical grid-based solvers suffer from the curse of dimensionality, continuous-time neural solvers require accurate spatial value gradients, and reinforcement-learning-based approaches often suffer from weak boundary anchoring and non-stationary adversarial policy optimization. We propose a discrete-time neural reachability framework for control-disturbance-affine systems that learns backward reachable tubes (BRTs) and backward reach-avoid tubes (BRATs) through Bellman-Isaacs value propagation. Our key idea is to combine equation-driven self-supervision with structured policy learning: rather than computing explicit PDE-gradients, we exploit the bang-bang structure of optimal safety interventions to construct approximate teacher actions from gradient-free value probes, converting adversarial actor learning into supervised policy learning. To stabilize long-horizon value propagation, we leverage the learned actor to train the value function backward from the terminal boundary using a windowed temporal curriculum, where each window is used as the boundary condition for the next window. Across benchmark problems up to 80 dimensions, our method learns accurate reachability value functions while improving stability over existing learning-based solvers. We further demonstrate observation-space scalability on F1-tenth racing with over 16,000-dimensional egocentric inputs. The learned safety filter generalizes zero-shot to unseen tracks and transfers to a physical RC car, achieving real-time robust collision avoidance.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.21819

Ctrl-A: Control-Driven Machine Learning using Adaptive Online Data Augmentation

Jesper B. Christensen, Ciaran Bench, Spencer A. Thomas, Hüsnü Aslan, David Balslev-Harder, Nadia A. S. Smith, Alessandra Manzin

原文摘要

We introduce ControlAugment (Ctrl-A), an automated data augmentation algorithm for image-vision tasks, which incorporates principles from control theory for online adjustment of augmentation strength distributions during model training. Ctrl-A eliminates the need for initialization of individual augmentation strengths. Instead, augmentation strength distributions are dynamically, and individually, adapted during training based on a control-loop architecture and what we define as relative operation response curves. Using an operation-dependent update procedure provides Ctrl-A with the potential to reduce the use of augmentation operations that negatively impact model performance, alleviating the need to manually engineer augmentation policies for new image classification tasks. Experiments on the CIFAR-10, CIFAR-100, SVHN-core, and Tiny ImageNet benchmark datasets using WideResNet-28-10 and ResNet architectures demonstrate that Ctrl-A provides competitive performance across a range of image classification benchmarks.

cs.CV · cs.AI · cs.LG · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2512.22793

Reach-Avoid Differential game with Reachability Analysis for UAVs: A decomposition approach

Minh Bui, Simon Monckton, Mo Chen

原文摘要

Reach-avoid (RA) games have significant applications in security and defense, particularly for unmanned aerial vehicles (UAVs). These problems are inherently challenging due to the need to consider obstacles, consider the adversarial nature of opponents, ensure optimality, and account for nonlinear dynamics. Hamilton-Jacobi (HJ) reachability analysis has emerged as a powerful tool for tackling these challenges; however, while it has been applied to games involving two spatial dimensions, directly extending this approach to three spatial dimensions is impossible due to high dimensionality. On the other hand, alternative approaches for solving RA games lack the generality to consider games with three spatial dimensions involving agents with non-trivial system dynamics. In this work, we propose a novel framework for dimensionality reduction by decomposing the problem into a horizontal RA sub-game and a vertical RA sub-game. We then solve each sub-game using HJ reachability analysis and consider second-order dynamics that account for the defender's acceleration. To reconstruct the solution to the original RA game from the sub-games, we introduce a HJ-based tracking control algorithm in each sub-game that not only guarantees capture of the attacker but also tracking of the attacker thereafter. We prove the conditions under which the capture guarantees are maintained. The effectiveness of our approach is demonstrated via numerical simulations, showing that the decomposition maintains optimality and guarantees in the original problem. Our methods are also validated in a Gazebo physics simulator, achieving successful capture of quadrotors in three spatial dimensions space for the first time to the best of our knowledge.

eess.SY · cs.AI · cs.GT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35862

Learning Force-Reactive Humanoid Loco-Manipulation

Sihang Li, Xiaofeng Guo, Jinkun Cao, Charlie Cheng, Ruihan Yang, Chen Feng, Guanya Shi, Haozhi Qi

原文摘要

Many everyday tasks require controlling contact force as well as motion. On a humanoid, this force must often be coordinated across both hands and the whole body, and cannot be specified by kinematic trajectories alone. Therefore, we present FLORE, a framework for learning force-reactive humanoid loco-manipulation. We collect robot-free demonstrations using two force-sensing UMI-style handheld grippers for hand motion and contact wrenches, and wearable trackers for body motion. A force-reactive diffusion policy takes vision, proprioception, and measured wrenches as input, then predicts actions and per-hand force commands. A hybrid compliance whole-body controller with force tracking executes these commands while maintaining balance: an integral of the force error measured by real force/torque (F/T) sensors lets each hand track force accurately along the commanded direction and remain compliant along the others. In simulation, the controller tracks per-hand force commands of varied magnitude and direction, with little loss in whole-body motion accuracy. Across three contact-rich tasks on the robot, the force-reactive policy succeeds in 80% of rollouts, compared with 26.7% for a position-only policy trained on the same demonstrations and executed by the same controller. For more details, please visit our website: https://louis-leee.github.io/FLoRe/.

cs.RO · cs.AI · cs.LG · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35851

Nonprehensile Robotic Catching Using Virtual Constraints

Aakash Khandelwal, Ranjan Mukherjee

原文摘要

The problem of nonprehensile robotic catching is addressed using virtual nonholonomic constraints (VNHCs); the motion of the robot end-effector is designed and controlled directly in terms of the states of the object before it is caught on the surface of the end-effector. In contrast to contemporary approaches, our approach avoids the need for object trajectory prediction, and consequent time-varying robot trajectory generation. With a focus on the planar case, we demonstrate that our VNHC approach can be used to catch an object on an end-effector at a desired height, with minimal relative normal velocity at the contact instant. Moreover, the VNHC function depends only on the desired catching height, and the same VNHC can be used for a range of initial conditions. We use model predictive control (MPC) to bring the object and end-effector to rest at a desired location after contact is made. The approach is validated in simulation.

eess.SY · cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.20967

Formalizing and Estimating Task-Space Complexity for Zero-shot Generalization

Jung-Hoon Cho, Heling Zhang, Siqi Du, Roy Dong, Cathy Wu

原文摘要

Generalization is crucial for modern control, but learning across tasks can be costly. Recent work shows that zero-shot transfer, in which a controller designed for a source task is directly deployed to a target task, can achieve strong performance. We formalize task-space complexity, which characterizes the difficulty of achieving sufficiently good performance across a collection of tasks, by casting source selection as a covering problem. We provide bounds on the complexity and demonstrate its applicability.

cs.LG · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35813

World Model for Energy Efficiency Maximization in Dynamic Metasurface Antennas

Abdolrasoul Sakhaei Gharagezlou, Mehdi Monemi, Matti Latva-aho, Mehdi Rasti

原文摘要

Dynamic metasurface antennas (DMAs) have emerged as a promising technology for realizing large-scale antenna arrays in next-generation wireless networks owing to their ability to enable dense and energy-efficient antenna architectures. However, jointly optimizing digital beamforming and DMA element weights under time-varying channel conditions remains challenging due to their coupled effects on the received signal and interference powers. We formulate this problem to maximize time-averaged energy efficiency (EE) under time-varying channel conditions, subject to transmit power constraints, Lorentzian phase limitations, and minimum rate requirements. To enable long-term planning and improve sample efficiency compared with model-free reinforcement learning, we develop a world model framework that learns latent system dynamics from interactions with the environment and jointly optimizes digital beamforming and DMA element weights through imagined trajectories. Furthermore, we incorporate feasibility-preserving action mappings and safety cost signals to ensure constraint satisfaction during policy learning. Simulation results demonstrate that the proposed framework improves the time-averaged EE by approximately 20% compared with the deep Q-network baseline, while achieving higher sample efficiency and more reliable constraint satisfaction.

cs.IT · eess.SY

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2609.21718

A Novel Path-Tracking Algorithm for Automated Tractor-Trailer Forward and Backward Maneuvers

Alexandre Lombard, Florent Perronnet, Théo Rigal, Nicolas Gaud, Abdeljalil Abbas-Turki

原文摘要

Fully autonomous tractor-trailer systems are increasingly deployed in logistics, agriculture, and industrial environments, where precise and robust path-tracking capabilities are essential. However, the articulation between the tractor and the trailer introduces additional nonlinearities and significantly complicates lateral and longitudinal control, particularly during reversing maneuvers. This paper introduces a novel path-tracking algorithm specifically designed for articulated vehicles with a single trailer. The proposed method combines a lateral control law applied at the trailer level with a short-horizon predictive adjustment of the tractor steering angle, ensuring stable convergence toward the desired path in both forward and backward motion. The approach is geometry-based and requires no per-vehicle calibration or training. Simulation studies in a high-fidelity physics simulator show steady-state or final lateral errors of $0.02$--$0.05$~m on straight-line, circular, and reverse docking trajectories, which is of the same order of magnitude as, or lower than, the errors reported in the literature. Real-world experiments on a small-scale tractor--trailer robot, with the whole controller running on a low-cost microcontroller, further confirm the feasibility of the approach, with lateral errors below 13~mm during repeated reverse docking maneuvers despite a pose detection latency of about 200~ms.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35569

ADACT: PDE-Constrained Thermal Hardware/Software Co-Design Adjoint-Driven Optimal Control via Finite Difference Discretization

Don Li

原文摘要

By virtue of the sheer compute demands of large-scale artificial intelligence (AI) and high-performance computing (HPC), modern multi-core microprocessors suffer from severe power density limits and thermal throttling, entailing an urgent need for principled methods for predictive, physics-informed dynamical thermal management (DTM). Here, we introduce a hardware/software co-design physics-informed PDE-constrained DTM throttling optimal control algorithm, ADACT (Adjoint-Driven Adaptive Co-Design Thermal Control), that minimizes thermal risk and cooling energy overhead while maximizing core execution throughput. The underlying physical temperature field is governed by a two-dimensional nonhomogeneous parabolic reaction-diffusion equation with spatially localized, time-varying power source terms and variable convective cooling subject to nonhomogeneous Dirichlet boundary conditions. We employ a discretization of the continuous state PDE via a Crank-Nicolson finite difference scheme with a 5-point discrete Laplacian operator on a uniform spatial mesh, which preserves unconditional numerical stability and $O(Δx^2 + Δy^2 + Δt^2)$ second-order temporal and spatial accuracy. To efficiently compute exact gradients, we construct and implement a continuous backward-in-time adjoint state method with L-BFGS as our optimization algorithm of choice. The accuracy and second-order convergence rates of both forward state and backward adjoint solvers are quantitatively verified via the Method of Manufactured Solutions (MMS), and gradient accuracy is validated using directional derivative Taylor tests. Numerical experiments demonstrate that ADACT dynamically throttles software power workloads and modulates hardware cooling efforts to strictly uphold physical thermal thresholds while maintaining operational throughput.

math.OC · cs.AR · eess.SY · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35296

Trim: Fee Level Matters More Than Skew in Lightning Routing

Huiping Sun, Peilu Guo, Yiming Zhu, Yueqi Wu, Zhong Chen

原文摘要

Lightning routing nodes lend their channel balance for a posted fee. Operators' tools move that fee with the balance around a hand-set level, and which part of the fee drives revenue is unclear. We present Trim, which models a channel as a two-sided inventory with costly refills. Its optimal fee is a level, set by demand and replenishment, plus a balance skew, the shadow price of liquidity. The level is first order: no fee rule beats the ceiling demand and replenishment set, and on exactly solved channels the best flat fee comes within $1.6\%$ of the optimum at the median, whereas a level off by a factor of two loses tens of percent. The skew is second-order insurance: it adds at most what the flow-balancing flat fee loses to refused forwards. So the level should be chosen: Trim's rule traces the revenue--success frontier, posts one level on every channel, and adds a skew only where refills stay frequent and costly. On a 2026 snapshot with synthetic payments, no tuned tool beats that frontier by over a few percent, and a skew changes gross revenue by a few percent.

cs.NI · cs.CR · cs.GT · eess.SY

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2606.01478

Crazyflow: An Accurate, GPU-Accelerated, Differentiable Drone Simulator in JAX

Martin Schuck, Marcel P. Rath, Yufei Hua, Abhishek Goudar, SiQi Zhou, Angela P. Schoellig

原文摘要

High-quality, large-scale synthetic data from simulations is becoming a cornerstone for pushing robots' capabilities. While aerial robotics simulators have evolved to support specialized needs such as fidelity, differentiability, and swarms independently, a unified platform that synthesizes data across all these domains is missing. In this work, we propose Crazyflow, a simulator designed to push the frontier of aerial robotics algorithm development, supporting model-based and data-driven methods, gradient-based and sampling-based approaches, single- and multi-agent systems, and state- and vision-based policy learning. Crazyflow combines high-fidelity dynamics with JAX for speed and differentiability, scaling to millions of environments or thousands of swarms with thousands of drones on single CPUs and GPUs, and beyond on clusters. Real-world experiments show Crazyflow supports both analytical-gradient-based policy learning, achieving high-accuracy tracking without domain randomization, and sampling-based obstacle avoidance at speeds exceeding half a billion steps per second. Breaking the traditional train-then-deploy paradigm, Crazyflow's performance enables in-flight reinforcement learning; we demonstrate this by throwing a physical drone into the air and training a recovery policy from scratch in 0.37 seconds, catching it before it hits the ground. By pushing accuracy, speed, and differentiability simultaneously, Crazyflow serves as an open-source resource for synthetic data generation, with emerging capabilities for large-scale parallelization for online, in-execution learning and optimization, opening the door to novel algorithm development.

cs.RO · cs.AI · cs.MA · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35070

Joint Dynamic Lane Reversal and Variable Speed Limit Control: A Graphormer-Based Approach

Hamed Araab, Abbas Ahmadi, Mohammad Hossein Basiri

原文摘要

Connected and automated vehicles could allow reassigning lane directions and speed limits on every road frequently, but the benefit hinges on how traffic reroutes, and one-way roads can isolate junctions. We formulate network-wide dynamic lane allocation and directional speed limit control with one-way roads, lane clearing before each reversal, and a navigable graph kept strongly connected at all times. An action-resolution algorithm grounded in graph orientation theory admits a proposal closing a direction only if the network stays strongly connected and otherwise relaxes or reverts it, keeping all junctions mutually reachable within the model, even during clearing. All road controllers share one transformer whose attention, Global Endpoint Relation-Aware Spatio-Temporal Self-Attention (GERASTSA), spans all road-time tokens, biased by the endpoint relations between roads. It is pretrained on action-conditioned traffic forecasting and fine-tuned with per-road proximal policy optimization under a mixed reward that also credits neighboring roads and the network. On a 6 x 6 grid with commuter rush hours, the learned policy makes about two thirds of roads one-way in the peak direction and cuts daily delay by 90% against the initial configuration held fixed and by 85% against a tuned two-way load balancer. Restricted to two-way roads at test time, it gains only about as much as this balancer, whereas its learned speed limits add delay. Pretraining roughly halves the updates needed to surpass the fixed configuration. Of the ablations, only removing the relational bias or the spatial embedding clearly matters, multiplying delay about fivefold.

eess.SY · cs.AI · cs.LG

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2608.25376

GPU-Accelerated Quantum Annealing-Inspired UAV Path Planning for Smart Agriculture

Maho Hirahara, Aohan Li

原文摘要

With the rapid advancement of smart agriculture, Unmanned Aerial Vehicles (UAVs) have become indispensable for efficient coverage and path planning in grid-mapped agricultural fields. However, optimizing UAV flight paths in large-scale environments remains a complex Nondeterministic Polynomial-time (NP)-hard problem. Traditional path planning methods are often constrained by local optima, limited scalability, and slow convergence, which significantly restrict their effectiveness in solving large-scale problems. To address these limitations, this paper proposes a novel optimization framework utilizing a Graphics Processing Unit (GPU)-parallelized Ising solver. Our method mimics the operational principles of quantum annealing on GPU hardware, enabling rapid search for the minimum-energy state of Ising models. Unlike physical quantum devices, which are often constrained by the number of qubits, our approach leverages the Fixstars Amplify (FA) platform to perform parallel annealing on highly parallelized GPUs. This enables the simultaneous evaluation of thousands of potential path candidates and vast state transitions. By leveraging large-scale parallel processing, this framework's core strength lies in minimizing computation time even as the problem scale increases. Simulation results demonstrate that our proposed method consistently identifies superior flight paths while maintaining stable computational performance compared with the genetic algorithm and simulated annealing method.

cs.ET · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34913

Quantile-Based Command-Reserve MPC for Current-Command Gimbal Pointing

Yaokun Lu, Yifan Li, Enmei Wang, Hao Teng, Pengwei Hu, Jianzhong Qiao

原文摘要

This paper studies the constrained line-of-sight pointing control problem of a two-axis current-command gimbal subjected to mismatched mechanical disturbances. Friction, torque ripple, current-response lag, model mismatch, and limited drive commands make accurate pointing more challenging yet interesting. A quantile disturbance predictor is proposed to compensate predictable acceleration residuals and describe their remaining uncertainty, with the aid of learning median and outer quantiles from state, command, and reference histories. A tube-inspired model predictive controller is proposed to reserve command headroom according to prediction uncertainty, with the aid of calibrated radii and stage-dependent input margins. The median improves the nominal prediction, while the radius adapts the command reserve without adding optimization variables. A finite-horizon tube inclusion result is established for a specified linear error model and fixed ancillary policy; its conditions are distinguished from the approximations used in hardware. Native command bounds and a final command limiter keep the sent commands within their prescribed range. On a two-axis embedded platform, four controller configurations are compared in seven repeated trials each. The proposed configuration achieves a two-dimensional pointing root-mean-square error of $3.94 \pm 0.10$ mrad with no command-limit samples in the evaluation windows. Its performance supports the combined use of residual prediction and adaptive command margins in the tested operating condition. Solver times and logged loop overruns are reported separately for the nominal 100 Hz implementation.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34849

Physics-Informed and Data-Driven Composite Disturbance Separation and Predictive Control for Airborne Electro-Optical Gimbals with Multiple Constraints

Yaokun Lu, Yifan Li, Zhixuan Zhang, Enmei Wang, Hao Teng, Jianzhong Qiao

原文摘要

This paper studies the constrained pointing control problem of airborne electro-optical gimbals subjected to composite periodic and aperiodic disturbances. Under carrier vibration, input delay, and actuator constraints, achieving line-of-sight stabilization and tracking is challenging. A disturbance-separated model predictive control (MPC) scheme is proposed to compensate these heterogeneous disturbances according to their physical characteristics. An adaptive periodic observer predicts multi-harmonic vibrations over the MPC horizon using linear-regression frequency estimation. A linear observer estimates the aperiodic disturbance, and a bounded long short-term memory (LSTM) network predicts its change over the input delay. Embedding periodic predictions into the MPC model while applying aperiodic compensation outside the optimizer limits amplification of aperiodic estimation errors and improves pointing accuracy under frequency changes and parameter mismatch. On the stated headroom envelope, input-set tightening provides feasible commands within the prescribed limits, and a regional practical tracking bound is established under explicit input-sequence and predicted-state margin conditions. A UAV-mounted two-axis gimbal platform is assembled for comparative indoor hovering experiments, complemented by simulations under frequency changes and parameter mismatch. In the reported trials, the proposed method achieves yaw and pitch relative-angle root-mean-square errors (RMSEs) of 0.770 degrees and 0.350 degrees, reducing tracking errors by 25.1% and 45.9% compared with linear active disturbance rejection control.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.05911

PREFER: Personalized Review Summarization with Online Preference Learning

Millend Roy, Agostino Capponi, Vineet Goyal

原文摘要

Product reviews significantly influence purchasing decisions on e-commerce platforms. However, the sheer volume of reviews can overwhelm users, obscuring information most relevant to their individual needs. Existing e-commerce summarization systems typically produce generic, static summaries that fail to account for the fact that (i) different users prioritize different product characteristics, and (ii) these preferences may evolve with interactions. To address these challenges, we propose an online learning framework that generates personalized summaries for each user by representing user interests in a latent aspect space and selecting preference-aligned review evidence. Our system then iteratively refines its understanding of user preferences by incorporating feedback on the generated summaries directly over time. We evaluate our framework on Amazon Reviews'23 using controlled feedback simulations and robustness checks with held-out-history, independent-embedding, and external LLM-based evaluators. These experiments show improvements in preference-aligned evidence selection under the evaluated feedback settings. A separate human-based study finds that scenario-conditioned summaries are preferred over generic (uniformly-aggregated) alternatives in 69.9% of paired comparisons.

cs.AI · cs.GT · cs.LG · eess.SY · math.OC

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.07763

SateBeam: Beam-Aware Radio Map Estimation With Physics-Consistent Parametric Modeling for Unknown Multiple Satellites

Xiucheng Wang, Nan Cheng, Zhisheng Yin, Conghao Zhou, Ruijin Sun

原文摘要

Dense low-Earth-orbit (LEO) networks increasingly reuse spectrum across moving satellites and overlapping beams, making a spatial radio map essential for interference diagnosis and coordination. Constructing such a map from sparse ground power measurements is difficult because the active satellite subset and its cardinality are unknown, candidate footprints can be highly coherent, and beam pointing, width, effective power, Doppler, and background must be inferred jointly. Known ephemerides do more than enumerate visible candidates. Propagated positions determine candidate-specific ranges, viewing geometry, and spatial footprints, while radial velocities induce distinct time-frequency signatures. Their joint spatial--Doppler structure rejects candidates whose geometry or time--frequency signature conflicts with the observations before continuous beam inference. SateBeam therefore screens all candidates through Doppler-resolved nonnegative fitting. GPU-accelerated grid correlations initialize continuous off-grid refinement; a Gamma-likelihood Bayesian information criterion (BIC) selects model cardinality, and backward deletion and exchange repair greedy support errors. Across paired real-ephemeris scenarios, SateBeam recovers the exact support in every case and reduces independent-query RMSE from $0.655$ to $0.050$ dB relative to orthogonal matching pursuit, with consistent advantages under model mismatch and larger candidate sets. The code is available at https://github.com/UNIC-Lab/SateBeam.

cs.IT · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34670

Compound Environmental Operating Stress and Cyber-Induced Pump Disruption in Water Distribution Networks: Matched Hydraulic Impacts, Service Continuity, and Sustained Recovery

Halil Dursunoglu, Fatma Dursunoglu

原文摘要

Digitally controlled water distribution systems must maintain service during disruptions, yet restoring water delivery does not necessarily ensure hydraulic recovery. This study evaluates cyber-induced pump outages under hypothetical environmental operating stress using two EPANET benchmark networks (Net1 and Net3) simulated with the Water Network Tool for Resilience. The experimental design comprises 288 simulation records (240 distinct configurations), including 192 verified attack scenarios, with four- and twelve-hour outages, six hydraulic configurations, and four mitigation strategies. All attack scenarios satisfied pump-shutdown verification and pre-attack hydraulic-equivalence criteria. Twelve-hour outages produced an average of 404.96 m$^3$ of unmet demand in Net1, while Net3 maintained full modeled demand delivery despite pressure deviations. Sustained pressure recovery occurred in 103 of 192 scenarios, with 65 exhibiting subsequent relapse, whereas all scenarios satisfied the post-outage water-service recovery criterion. Combined mitigation eliminated unmet demand in evaluated Net1 scenarios, while outage shortening achieved the highest sustained-pressure-recovery count. These findings highlight network-dependent resilience and the distinction between service restoration and hydraulic recovery. Environmental stress scenarios represent sensitivity assumptions rather than empirically calibrated climate impacts.

cs.CR · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.03116

Shooting for Contact: Contact-Implicit Multiple Shooting for Dynamic Motion Retargeting

Sergio A. Esteban, Jason H. K. Siu, Derrick Mach, Junheng Li, Vince Kurtz, Joel W. Burdick, Aaron D. Ames

原文摘要

Motion retargeting approaches often prioritize kinematic similarity over whole-body dynamics, contact consistency, and actuation limits, yielding references that are difficult for reinforcement learning (RL) policies to reproduce, particularly for contact-rich behaviors. We present a contact-implicit, direct simulation-based multiple shooting (DSMS) framework that transforms kinematically feasible references into dynamically feasible whole-body trajectories. By embedding a differentiable simulator within a nonlinear program, DSMS resolves contact, friction, impacts, self-collision, and joint limits internally while enforcing tracking, actuation, and task constraints without prescribing a contact schedule or introducing explicit contact constraints. Compared with existing retargeting methods, DSMS accelerates motion-imitation RL training and yields policies with high success rates and low tracking error. We further demonstrate zero-shot sim-to-real transfer on the Unitree G1 through command-conditioned walking, jogging, and contact-rich crawling, and a highly dynamic 180-degree jump-turn.

cs.RO · eess.SY

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2608.23444

Fair Dynamic Operating Envelopes using Distributed Multi-Period Optimal Power Flow and Jain Index for Active Distribution Networks

Pedro Salomão Quessongo, Daniel Gebbran, Clodomiro Unsihuay-Vila

原文摘要

Dynamic operating envelopes (DOEs) are increasingly used to publish time-varying export limits that keep distribution networks within operational limits. Purely technical DOE allocation, however, can systematically privilege electrically favorable prosumers, while embedding fairness directly into a single-period optimal power flow (OPF) objective mixes network feasibility, equity and efficiency in a way that obscures the cost of fairness. This paper proposes a two-stage, multi-period framework that addresses both of these. Initially, a technical distributed OPF computes network-feasible export envelopes. The subsequent stage then applies a dynamic aggregate export budget and redistributes capacity through cumulative proportional fairness, limiting the additional curtailment by an admissible efficiency budget. The resulting fair DOEs are treated as first-stage decisions, while battery storage provides scenario-dependent recourse under demand and renewable uncertainty. The operational problem is solved by a calibrated regional alternating direction method of multipliers (ADMM) on a lossless LinDistFlow model and independently validated using AC power flow. We observe a trade-off between increases in curtailment \emph{versus} improvements on fairness, as expected, reaching Jain fairness indices close to unity. Results show that considering both storage (which alleviates curtailment impact) and multi-period fairness (which increases curtailment) is an interesting approach for modern DOE design, which in turn requires a multi-period, co-designed approach.

eess.SY · cs.CE

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2602.07215

Multi-Agentic AI for Fairness-Aware and Accelerated Multi-modal Large Model Inference in Real-world Mobile Edge Networks

Haiyuan Li, Hari Madhukumar, Shuangyi Yan, Yulei Wu, Dimitra Simeonidou

原文摘要

Generative AI (GenAI) has transformed applications in natural language processing and content creation, yet centralized inference remains hindered by high latency, limited customizability, and privacy concerns. Deploying large models (LMs) in mobile edge networks emerges as a promising solution. However, it also poses new challenges, including heterogeneous multi-modal LMs with diverse resource demands and inference speeds, varied prompt/output modalities that complicate orchestration, and resource-limited infrastructure ill-suited for concurrent LM execution. In response, we propose a Multi-Agentic AI framework for latency- and fairness-aware multi-modal LM inference in mobile edge networks. Our solution includes a long-term Global Planning Agent, a short-term Prompt Scheduling Agent, and multiple on-node LM Deployment Agents, all powered by foundation language models. These agents cooperatively optimize prompt routing and LM deployment through natural language reasoning over runtime telemetry and historical experience. To evaluate its performance, we further develop a city-wide testbed that supports network monitoring, containerized LM deployment, intra-server resource management, and inter-server communications. Experiments demonstrate that our solution reduces average end-to-end latency by 42.9% compared with a state-of-the-art online-learning baseline and by 80.6% compared with the full-activation baseline, while maintaining a mean service-type Jain's index of 0.925. Moreover, our solution adapts quickly without fine-tuning, offering a generalizable solution for optimizing GenAI services in edge environments.

eess.SY · cs.AI · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34538

Dual Gradient Consensus for Adaptive Learning to Transient Out-of-Distribution Disturbances in Quadrotor Control

Mingtian Tan, Vasileios Tzoumas

原文摘要

Quadrotor trajectory tracking under unmodeled disturbances often relies on online residual adaptation, but short out-domain residuals can corrupt the learned disturbance model when constant-step updates treat every residual gradient uniformly. We propose Dual Gradient Consensus (DGC), an adaptive-learning method for mixed in-domain/out-domain disturbances that retains the random Fourier feature model, offline BPTT policy, and least-squares adaptation of a prior domain-adaptive residual policy (DAPL) while modifying the online update step size. DGC compares each residual gradient with long- and short-term gradient memories, classifies the current adaptation regime, and uses a confidence map to suppress unreliable updates while recovering adaptation after persistent domain shifts. We evaluate DGC in numerical quadrotor trajectory tracking under four residuals that mix an in-domain component with short out-domain events. Compared with $L_1$-augmented MPC, RFF-based MPC, constant-step DAPL, and a frozen-parameter baseline, DGC achieves the lowest mean position RMSE in all four cases.

cs.RO · eess.SY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34509

Symbolic Direct Construction of Augmented Systems for Levenberg-Marquardt Power Flow

Tianshi Cheng, Venkata Dinavahi

原文摘要

Levenberg-Marquardt power flow (LMPF) addresses ill-conditioned equations and provides least-squares stationary points for infeasible operating requests. Conventional implementations form damped normal equations, where J^T J introduces fill, squares the spectral condition number of the undamped part, and adds sparse-product cost. We propose an augmented LMPF formulation with a symbolic-permutation-based Jacobian operator that fills the augmented blocks directly. We prove that the matrix is symmetric quasi-definite for positive damping, enabling fixed-order, pivot-free QDLDL factorization. This property also motivates the solver choice: KLU value-only refactorization can reuse an unsuitable pivot order, forcing fresh full factorizations on difficult LMPF trajectories. We further introduce one-probe adaptive trust-region initialization for the radius and damping. Direct assembly is 7.5-11.9x faster than the fastest coordinate-list path and 18.0-58.8x faster than cached normal-equation sparse-product formation; QDLDL reduces numerical refactorization and triangular-solve time by up to 3.40x on identical matrices. Adaptive initialization solves flat-started RTE cases in 19-25 factorizations, 7.5-11.6x faster than reproduced three-step LM. On case2383wp, our 84-iteration run performs 84 factorizations and 168 resolves in 237 ms, versus published times of 200.780-441.72 s with fewer outer iterations, corresponding to speedups of 847-1860x. These results require complete factorization and resolve accounting and reveal initialization and legacy-parameter effects. DC-initialized NR exhibits fast quadratic convergence throughout every tested solvable loading range, motivating clearer, reproducible definitions of ill-conditioned power-flow benchmarks.

eess.SY · cs.PF · math.NA

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34456

Trajectory Generation and Control for Quadrotors

Sandesh Thapa

原文摘要

In this project, I explored controller design and trajectory generation for a quadrotor. Differential flatness was used for trajectory generation based on one of the references. A minimum snap trajectory is generated from the given algorithm based on waypoint navigation, and a simple proportional plus derivative (PD) controller is used to track the trajectories. Simulation results are presented which generate smooth trajectories for position and velocity of the quadrotor.

eess.SY · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.34454

Nonlinear Control of Aerial Manipulator

Sandesh Thapa

原文摘要

This paper presents two controllers for a quadrotor equipped with a two-DOF robot manipulator that physically interacts with the environment. Cartesian impedance and adaptive sliding mode controllers are implemented and compared for a simple hovering task in the presence of some bounded disturbance. A full dynamic model is developed, and the two above controllers are implemented with the same initial conditions and desired position.

eess.SY · cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2506.13498Google Research

A Survey on Imitation Learning for Contact-Rich Tasks in Robotics

Toshiaki Tsuji, Yasuhiro Kato, Gokhan Solak, Heng Zhang, Tadej Petrič, Francesco Nori, Arash Ajoudani

原文摘要

This paper comprehensively surveys research trends in imitation learning for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the environment, represent a central challenge in robotics due to their nonlinear dynamics and sensitivity to small positional deviations. The paper examines demonstration collection methodologies, including teaching methods and sensory modalities crucial for capturing subtle interaction dynamics. We then analyze imitation learning approaches, highlighting their applications to contact-rich manipulation. Recent advances in multimodal learning and foundation models have significantly enhanced performance in complex contact tasks across industrial, household, and healthcare domains. Through systematic organization of current research and identification of challenges, this survey provides a foundation for future advancements in contact-rich robotic manipulation.

cs.RO · cs.HC · cs.LG · eess.SY

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arXiv ↗PDF ↗