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Related Articles from SNS
Preferential orientation of slender elastic floaters in gravity waves
arXiv:2604.08323v2 Announce Type: replace Abstract: Slender floaters drifting in propagating gravity waves slowly rotate towards a preferential state of orientation with respect to the angle of incidence. This angular drift arises from a wave-induced, second order mean yaw moment. We develop a diffractionless, hydro-elastic theory to compute this mean yaw moment for a thin, flexible structure whose width and thickness are small compared with the wavelength.
Learning All-Terrain Locomotion for a Planetary Rover with Actively Articulated Suspension
arXiv:2606.06790v1 Announce Type: new Abstract: This paper presents ERNEST, a four-wheeled planetary rover concept equipped with a two-degree-of-freedom Active Gimbal Suspension that combines yaw and roll actuation to enable wheel reconfiguration, steering, and active load redistribution. A single neural network controller, trained to track a desired path across challenging terrain, fully unlocks the capabilities of this actuated suspension system for autonomous obstacle negotiation. A...
AirDreamer: Generalist Drone Navigation with World Models
new Abstract: Navigating a drone in unseen and cluttered environments requires reliable generalization to unseen scene layouts and understanding of environmental structure relative to the robot's capabilities. Previous methods, which assume the same environment configuration, often rely heavily on human-designed perception pipelines and predefined rules to guide the robot toward the target. This process is environment-dependent and generalizes poorly across environments.
Neural Radiated-Noise Fields for Unmanned Underwater Vehicle Noise Spectrum Prediction in Three-Dimensional Scenes
Announce Type: cross Abstract: Radiated noise in unmanned underwater vehicles (UUVs) is an important indicator for characterizing acoustic signatures and evaluating platform performance. To address the strong dependence of traditional physics-based modeling and numerical simulation methods on target structural information and environmental boundary conditions, and their inability to achieve continuous spatial spectrum-response modeling in three-dimensional scenes, this paper proposes a...
RiskFlow: Fast and Faithful Safety-Critical Traffic Scenario Generation
arXiv:2606.06423v1 Announce Type: new Abstract: Safety-critical traffic scenario generation is essential for evaluating autonomous driving systems under rare but high-risk interactions. Existing diffusion-based methods offer strong controllability in closed-loop generation, but their iterative denoising process is computationally expensive and may accumulate sampling and guidance errors over long rollouts, causing unrealistic motion artifacts such as jitter, abnormal acceleration, and...
'The easiest market to manipulate': Why yellow car...
As the World Cup arrives in the U.S., a controversial wager that has been frequently targeted by match-fixers and banned in many jurisdictions remains on the books in some states. Yellow cards have been at the center of gambling schemes investigated in recent years by top leagues in Australia, Europe and the U.S. Some of the investigations resulted in criminal charges and lifetime bans, while a high-profile case in the English Premier League disrupted a potential multimillion-dollar transfer...
Reinforcement Learning Position Control of a Quadrotor Using Soft Actor-Critic (SAC)
arXiv:2512.18333v2 Announce Type: replace Abstract: This paper proposes a new Reinforcement Learning (RL) based control architecture for quadrotors. With the literature focusing on controlling the four rotors' RPMs directly, this paper aims to control the quadrotor's thrust vector. The RL agent computes the percentage of overall thrust along the quadrotor's z-axis along with the desired Roll ($\phi$) and Pitch ($\theta$) angles.
Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control
Announce Type: cross Abstract: While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires an interpretable chain of reasoning that connects physical evidence to structured control architectures. Here, we present a self-evolving scientific-agent workflow, driven by large language models and iterative code generation, that automates controller construction while preserving strict interpretability and...
ERGeoBench:A Comprehensive Benchmark for Embodied Reasoning and Geo-localization in Multimodal Large Language Models
arXiv:2605.31251v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong potential as embodied agents, yet embodied geo-localization remains underexplored due to the lack of fine-grained evaluation. We introduce ERGeoBench, a diagnostic benchmark for vision-driven embodied geo-localization. ERGeoBench evaluates models under three progressive settings -- single-view, panorama-view, and embodied-view -- where agents may actively acquire observations through...
Self-Evolving Scientific Agent Discovers Generalizable Physically-Reasoned Fluid Control
Announce Type: new Abstract: While data-intensive deep reinforcement learning can optimize complex control policies, scientific discovery in physical systems fundamentally requires an interpretable chain of reasoning that connects physical evidence to structured control architectures. Here, we present a self-evolving scientific-agent workflow, driven by large language models and iterative code generation, that automates controller construction while preserving strict interpretability and...