6540 papers
The paper introduces MARLIN, a decentralized reservoir management framework that combines multi-agent reinforcement learning inspired by starling murmurations with LLM-guided reward shaping to effectively handle environmental uncertainties, significantly improving flood response times and computational efficiency compared to traditional methods.
This paper introduces ROSflight 2.0, a modular, open-source ROS 2-based autopilot ecosystem designed to lower barriers for UAV research and accelerate the transition from simulation to hardware, featuring a lean architecture that successfully controls multirotors at 400 Hz with all loops running on a companion computer.
ROSplane 2.0 is an open-source, ROS 2-based fixed-wing autopilot framework designed by researchers to accelerate UAV experimentation through a lean, modular architecture, enhanced control algorithms, and a streamlined aerodynamic modeling pipeline that simplifies the transition from simulation to real-world testing.
This paper introduces HVAC-EAR, a novel system that reconstructs intelligible human speech from low-resolution, noisy pressure data in HVAC systems using a complex-valued conformer, demonstrating significant privacy risks by achieving clear eavesdropping up to 1.2 meters in real-world deployments.
PriorWeaver is an interactive visualization system that simplifies Bayesian prior elicitation for novice analysts by allowing them to iteratively construct and refine datasets reflecting their beliefs, resulting in priors that are more aligned with their expectations compared to traditional methods.
This paper proposes a bio-inspired, digital pheromone-based framework that mimics ant colony behavior to dynamically classify industrial production lines as In-Control or Out-of-Control and forecast maintenance needs by aggregating real-time temperature data, threat signals, and decaying environmental scores into a single adaptive indicator.
This paper presents "Pre/Absence," a virtual reality experience that leverages the dialectic of presence and absence to transform the interpretation of lost architectural heritage from static factual summaries into a nuanced, emotionally engaging narrative that fosters deeper cultural awareness and critical reflection on the evolving meanings of heritage.
The paper introduces BOUND, an open-world object detector that advances beyond simple "unknown" labeling by inferring coarse-grained, hierarchical categories for unseen objects to enable more informed decision-making while maintaining high performance on known classes.
This paper proposes a robust, data-driven framework that utilizes constrained nonlinear optimization with iteratively reweighted least squares and sliding-window strategies to enable accurate online estimation of quadrotor motor efficiency for applications in fault detection and predictive maintenance.
This paper establishes conservative probabilistic bounds for the spectrum of admittance matrices and linear power flow models under uncertain network parameters by leveraging random matrix concentration inequalities, thereby providing a theoretical framework to quantify approximation errors and analyze how uncertainty concentrates at critical nodes.
This paper presents ZipLex, a formally verified framework in Scala that achieves linear-time invertible lexical analysis with longest-match semantics by combining a novel token sequence abstraction with verified data structures like zippers and memoization, demonstrating performance comparable to unverified tools while providing rigorous proofs of correctness and invertibility.
This paper presents a sample-based hybrid mode control framework that formulates mode selection, switching timing, and duration as an integer optimization problem to achieve asymptotically optimal, reactive switching between non-differentiable and algorithmic control strategies for complex robotic tasks.
This paper proposes a multi-UAV flood monitoring strategy using Centroidal Voronoi Tessellation with a Gaussian Mixture of Density Functions to model inundation areas, demonstrating through ROS/Gazebo simulations that this approach achieves superior coverage rates and spatial distribution compared to conventional axis-aligned Gaussian models across various fleet sizes.
This paper introduces Consensus Complementarity Control Plus (C3+), an enhanced contact-implicit model predictive control algorithm that enables a complete robotic pipeline to robustly and efficiently push diverse single and multi-object configurations to target poses in real-time, achieving a 98% success rate on hardware.
This paper introduces AURASeg, an attention-guided segmentation framework featuring a Residual Boundary Refinement Module and an Attention Progressive Upsampling Decoder to enhance drivable-area boundary precision and multi-scale feature representation for onboard robot navigation, demonstrating superior performance on multiple datasets and successful deployment on a Jetson Nano.
This paper proposes and evaluates four augmented reality indicators designed to visualize a robot's field of view, finding that allocentric indicators placed in the task space and egocentric indicators modifying the robot's eyes effectively improve human accuracy in collaborative tasks while maintaining high confidence and low cognitive load.
This paper resolves a key open question from Gorantla et al. by deriving explicit upper bounds on the number of item copies required to guarantee envy-free allocations for arbitrary numbers of agent groups and item types, using a novel constructive technique that extends to both chores and continuous fair division settings.
This paper introduces CULTIVate, a comprehensive benchmark and evaluation framework designed to assess the cultural faithfulness of text-to-image models in depicting social activities across 16 countries, revealing significant performance disparities between Global North and South regions and demonstrating that its proposed metrics align more closely with human judgment than existing standards.
ExpReS-VLA is a specialized Vision-Language-Action model that enables rapid, memory-efficient on-device adaptation to specific robotic tasks by combining compressed experience replay, retrieval-augmented generation, and a novel contrastive loss to prevent catastrophic forgetting while significantly improving performance on both spatial and long-horizon benchmarks.
The paper introduces CAVER, a novel robot equipped with a specialized audio-exciting end-effector, a combined audiovisual representation, and a curiosity-driven exploration algorithm that efficiently learns object correlations to improve material classification and audio-only imitation tasks.