As autonomous robotic systems become increasingly mature, users will want to specify missions at the level of intent rather than in low-level detail. Language is an expressive and intuitive medium for such mission specification. However, realizing language-guided robotic teams requires overcoming significant technical hurdles. Interpreting and realizing language-specified missions requires advanced semantic reasoning. Successful heterogeneous robots must effectively coordinate actions and share information across varying viewpoints. Additionally, communication between robots is typically intermittent, necessitating robust strategies that leverage communication opportunities to maintain coordination and achieve mission objectives. In this work, we present a first-of-its-kind system where an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV) are able to collaboratively accomplish missions specified in natural language while reacting to changes in specification on the fly. We leverage a Large Language Model (LLM)-enabled planner to reason over semantic-metric maps that are built online and opportunistically shared between an aerial and a ground robot. We consider task-driven navigation in urban and rural areas. Our system must infer mission-relevant semantics and actively acquire information via semantic mapping. In both ground and air-ground teaming experiments, we demonstrate our system on seven different natural-language specifications at up to kilometer-scale navigation.
This study investigates interfacial mass and thermal transport across a steady-state flat condensing interface under non-equilibrium thermodynamic conditions. A phase-change driven nanopump, comprising liquid argon placed between parallel platinum plates, has been studied using non-equilibrium molecular dynamics simulations. This setup allows for a continuous examination of steady-state evaporation and condensation phenomena as the system achieves a statistically steady transport. We examined temperature profiles and the associated temperature jumps that occur within the finite interfacial region under various heat fluxes. We explored the correlation between these temperature jumps and the energetics of atoms crossing the interface. Hertz-Knudsen & Schrage models, widely used by many to calculate interfacial mass transport, use the mass accommodation coefficient(s) to compute mass flux. This research utilizes a Lagrangian framework to decompose the total mass flux into its constituent evaporation and condensation components. The study further calculates the evaporation and condensation coefficients using both the models and measured probabilistic values from the Lagrangian framework, providing a comparative analysis of the two approaches. This study presents a comprehensive analysis of net condensation at the liquid-vapor interface using a Lagrangian framework, challenging the applicability of classical kinetic theory models. Notably, the Schrage model exhibits a strong agreement with the Lagrangian framework, reinforcing its relevance in modeling interfacial mass transport.
This letter presents BEASST (Behavioral Entropic Gradient-based Adaptive Source Seeking for Mobile Robots), a novel framework for robotic source seeking in complex, unknown environments. Our approach enables mobile robots to efficiently balance exploration and exploitation by modeling normalized signal strength as a surrogate probability of source location. Building on Behavioral Entropy (BE) with Prelec's probability weighting function, we define an objective function that adapts robot behavior from risk-averse to risk-seeking based on signal reliability and mission urgency. The framework provides theoretical convergence guarantees under unimodal signal assumptions and practical stability under bounded disturbances. Experimental validation across DARPA SubT and multi-room scenarios demonstrates that BEASST consistently outperforms state-of-the-art methods and exhibits strong robustness to noisy gradient estimates while maintaining convergence. BEASST achieved 15% reduction in path length and 20% faster source localization through intelligent uncertainty-driven navigation that dynamically transitions between aggressive pursuit and cautious exploration.
Rapid distribution of diagnostic testing and countermeasures in community settings is a persistent challenge during pandemics. During the coronavirus disease 2019 (Covid-19) public health emergency, the Increasing Community Access to Testing, Treatment, and Response program at the U.S. Centers for Disease Control and Prevention provided free Covid-19 testing through retail pharmacies. The program enabled timely diagnosis for uninsured individuals and maintained a robust pharmacy network with the capacity to serve the entire U.S. population. This public-private partnership model can be employed for future public health emergency response, surveillance, research, and prevention.
As climate change intensifies, understanding the effects of climate hazards such as wildfires, hurricanes, coastal and riverine flooding, tornadoes, heat waves, and extreme snowstorms is key to understanding patterns of inequality. In this introduction, we review a multidisciplinary literature on the social, political, and economic consequences of climate hazards for individuals and communities in the US. We explore how the risk of exposure to climate hazards is unequal across places and demographic groups and examine how inequalities emerge in the process of recovery after climate disasters. We describe how different political actors shape climate policy as well as the role that government policies and programs play in mitigation and recovery efforts. The articles in this issue push novel research avenues for understanding the intersection of disasters and inequality. We conclude our introduction by discussing the challenges and key directions for future research.
