Papers

Oversized heating and cooling equipment can unnecessarily increase up-front costs, energy costs, pollutant emissions, and strain on electrical infrastructure. This paper develops two data-driven methods for estimating heating loads at design conditions to improve equipment sizing. One method uses smart thermostat data; the other uses utility bills. We test the methods on a dataset that we gathered from 74 detached single-family houses in five United States climate zones. The dataset includes smart thermostat time-series data, monthly utility bills, weather data, existing equipment specifications, and Manual J design load calculations (the United States industry standard) purchased from practitioners. The two methods have strong goodness-of-fit statistics individually and show fair agreement with each other. On average over the 74 houses, existing heating equipment is 2.75 (2.5th to 97.5th empirical percentile: 1.34-6.52) times larger than the thermostat-method estimate and 2.96 (1.36-7.76 times larger than the bill-method estimate. As implemented by practitioners, the Manual J estimate is 2.26 (0.87-5.21) times larger on average than the thermostat-method estimate and 2.35 (1.02-5.28) times larger than the bill-method estimate. We discuss prospects for implementing the data-driven methods at scale and for incorporating data-driven sizing into industry standards.

Environments are increasingly populated by multiple robots performing independent tasks with limited prior knowledge of each other. Deploying such multi-agent systems presents significant challenges. Specifically, shifts in deployment states compared to training data can lead to poor policy performance and compromised safety. While safety shields exist to mitigate these risks, they are typically reactive, which degrades performance near unseen obstacles,and centralized, limiting their scalability. To address this, we propose a decentralized framework that integrates predictive shielding with model-based finite horizon Q-learning. This approach allows agents to safely adapt their pre-trained policies during deployment. Furthermore, to mitigate livelocks in symmetric scenarios, we introduce a communication- free protocol for conflict resolution

This research proposes a model-free inverse approach for identifying the nonlinear anisotropic single-valued magnetic constitutive characteristics of soft magnetic materials from boundary measurements. Using the harmonic approximation, the internal magnetic flux density B and magnetic field strength H are independently reconstructed from the measured boundary data, thus avoiding the limitation of using a predefined parametric B–H mapping function. Subsequently, the reconstructed fields are spatially averaged along selected collection lines to recover the B–H characteristics of the principal directions. Numerical results demonstrate high reconstruction accuracy for unperturbed data and good robustness against Gaussian perturbations of the boundary measurements, with the overall mean error remaining below 5% for a 10% noise level. These results indicate that under the harmonic-field assumptions, the field-based reconstruction provides a simple and computationally efficient framework for non-invasive identification of nonlinear anisotropic magnetic constitutive characteristics.

This paper proposes an end-to-end generative framework for efficiently solving multi-period and multi-scenario stochastic model predictive control (SMPC) problems under nonlinear AC power-flow constraints. Conventional deterministic neural surrogates rely on a single-shot prediction, which makes reliable feasibility difficult to achieve because one dispatch trajectory must simultaneously satisfy nonlinear constraints across all scenarios and time periods. To address this limitation, a conditional stochastic neural generator (CSNG) is developed to produce multiple candidate dispatch trajectories for each uncertainty instance, enabling the recovery of a feasible and economical solution through candidate selection. A feasibility-aware self-supervised distribution-shaping scheme is further introduced to promote constraint satisfaction, candidate diversity, and operating economy without requiring computationally expensive SMPC solution labels, while mitigating candidate collapse in generative ACOPF learning. To support efficient end-to-end training, a constraint-aware differentiable architecture is introduced. It employs a projection mechanism to exactly enforce box and ramping constraints while preserving informative gradients near active bounds, together with a differentiable equality-completion surrogate for efficient AC power-flow reconstruction. Case studies on the IEEE 14- and 118-bus systems demonstrate $100\%$ feasibility, optimality gaps below $2\%$, and computational efficiency suitable for intraday dispatch. The implementation is publicly available at https://github.com/JieZhu6/Generative_SMPC.

Part I of this paper presented a decentralised framework for certifying small-signal stability in AC grids using frequency-domain quadratic constraints on individual grid subsystems. Part II extends the framework to a broader class of networks that contain unstable subsystems. In particular, we show that such unstable subsystems arise in many common scenarios, even when the aggregate system is stable and well behaved. Such subsystems must be stabilised by the closed-loop network interconnection, which complicates decentralised stability analysis. A stable hybrid representation resembling the power (PQ) model at low frequencies and the impedance (IV) model at high frequencies is then defined, to which the stability framework of Part I can be applied, allowing plug-and-play compatible conditions to be formulated. Furthermore, we show that at low frequencies, the characteristic loci in the Nyquist plot of the return ratio in this hybrid representation decouple into unbounded and bounded branches along the classical $P$-$\delta$ / $Q$-$V$ separation, and give sufficient conditions on each branch for ensuring system stability. The results are validated on the Kundur two-area system, where stability is certified, with a decentralised, plug-and-play compatible grid code covering the frequency ranges in which electromagnetic interactions arise.

The rise of renewable generation in AC power systems calls for small-signal stability assessment methods that do not rely on centralised, system-wide models. In this two-part paper, we present a decentralised stability framework based on frequency-domain conditions imposed on local subsystems (many admitting equivalent graphical interpretations) which collectively imply the stability of the entire grid. The framework generalises many previously reported results, with added flexibility and plug-and-play compatibility provided by the additional degrees of freedom of the imposed conditions, the ability to mix conditions across frequency ranges, conditions on extended subsystems that couple buses to their connecting lines and loads, and a method to incorporate unstable subsystems. Detailed, heterogeneous device and line models are accommodated in the frequency domain using impedance, admittance, and power-flow models, making the framework suitable as a foundation for grid codes that enable plug-and-play functionality in inverter-dominated grids. Part I focuses on stable impedance and admittance representations and Part II extends the results to unstable subsystems, which are shown to be fundamentally present in many grid models. The results of Part I are validated on a modified 9-bus case study, demonstrating the reduced conservatism and significance of the proposed approach.

Battery cell design must balance energy density, fast charging, and degradation, yet these metrics evolve over different time scales and are costly to optimize jointly. We develop a timescale-aware surrogate-assisted framework that evaluates beginning-of-life volumetric energy density and 10–80% charging time together with state-of-health (SOH) loss over 200 cycles. Physics-based simulations of 1501 cell designs generate 1427 quality-controlled samples across 12 manufacturing-relevant parameters. Objective-specific surrogates support total-order Sobol analysis, which reveals distinct parameter rankings across the three metrics. A cross-objective rank-union strategy then retains variables influential to at least one objective before evolutionary Pareto optimization. Re-evaluation of the optimized candidates using the original physics-based models confirms designs that outperform the reference cell in all three metrics. Among these jointly improving candidates, the objective-wise best solutions, attained by different designs, can reduce SOH loss by 99.31%, increase volumetric energy density by 12.49%, and shorten charging time by 28.73%. The framework makes Pareto exploration across disparate electrochemical timescales computationally tractable, thereby enabling systematic multi-objective optimization of battery cell design.

Controller design for physical systems directly from data is of tremendous interest in both the industry and academia. However, the research on direct data-driven control has thus far mainly focused on discrete-time linear time-invariant systems. To make the highly non-trivial step to continuous-time nonlinear systems, this paper explores an intermediate route through the framework of linear parameter-varying (LPV) systems. LPV systems can be used as a convenient surrogate for nonlinear systems to achieve systematic analysis and controller design. In this work, we generalize the LPV Fundamental Lemma for discrete-time systems towards a class of continuous-time LPV systems, achieving data-driven representations that can be used to design continuous-time LPV controllers.

This paper addresses the mathematical modeling and control of an aerial manipulator system comprising a quadrotor as the uncrewed aerial vehicle and a robotic arm as the manipulator. The dynamic model is established by identifying the overall center-of-mass velocity and the system's orientation as constraints, yielding a simplified, two-decoupled subsystems: a locked (overall translation) subsystem and a shape-space (actuation or overall rotation) subsystem, both subjected to external disturbances. Since the system is mechanically coupled, a critical challenge arises where constant bounded disturbances in the first subsystem manifest as time-varying, state-dependent disturbances in the second subsystem. Given that the quadrotor is inherently unstable, the movement of the robotic manipulator (RM) during flight can further jeopardize the stability of the entire QRM system if these disturbances and coupling effects are not effectively managed. To address this, we present a continuous nonlinear disturbance observer-based feedback control law, which enables the independent control of each subsystem while systematically eliminating cross-coupling effects. The efficacy of the proposed controller is validated through multiple simulations emulating practical operating conditions, thereby substantiating its real-world applicability and highlighting the core contributions of this work.

Inter-satellite links constitute a critical enabler for the autonomous operation of global navigation satellite systems (GNSSs).Nevertheless, onboard routing still poses substantial challenges, stemming from the frequent topology variations of GNSS constellations and the constraint of limited onboard computational resources for routing calculation. This paper develops a routing and topology-control framework tailored to the time-slotted structure of GNSS inter-satellite networks. By exploiting the fact that each satellite communicates with at most one scheduled peer in a slot, the proposed routing method propagates future delivery information backward over the slot sequence and directly determines whether data should be stored or forwarded. This structure avoids massive end-to-end path searches and enables topology generation and routing computation to proceed concurrently. The same delivery information is further introduced into topology construction so that link scheduling can directly account for end-to-end communication performance. A master-satellite autonomous orbit-determination scenario is used to demonstrate this topology–routing co-construction mechanism. In a 24-h, 30-satellite BeiDou scenario, the proposed routing method achieves routing performance comparable to Contact Graph Routing while reducing routing computation time by approximately 1500 times. The co-constructed topology further improves all-to-all communication and both master-related traffic directions while preserving strong inter-satellite ranging performance. These results fill a critical gap between autonomous topology planning and autonomous routing computation, supporting more complete onboard autonomous networking for future GNSSs.

In this study, we show that a continuous-time risk-sensitive control problem is equivalent to a Rényi divergence minimization problem over trajectory path measures. Reformulating stochastic optimal control as probabilistic inference via Kullback-Leibler (KL) divergence minimization avoids the computational intractability of the Hamilton-Jacobi-Bellman equation. However, standard KL control is inherently risk-neutral, and recent minimax extensions remain restricted to risk-averse settings. Our equivalence result resolves this limitation by offering a unified probabilistic framework for arbitrary risk attitudes in continuous-time nonlinear systems. Based on Girsanov theorem, we explicitly map the risk sensitivity to the Rényi divergence order, deriving a noise-dependent control penalty scaled by risk preference. This formulation seamlessly modulates tail-weighting behaviors, interpolating between zero-forcing for risk-averse policies and mass-covering for risk-seeking policies. These findings bridge stochastic control and information-theoretic inference, providing a foundation for sampling-based control algorithms.

This paper develops a spectral Koopman-Hopf framework for adversarial reachability analysis of nonlinear systems. By lifting the nonlinear drift dynamics into Koopman eigenfunction coordinates, the proposed approach transforms the original state-dependent Hamilton-Jacobi-Isaacs (HJI) equation into an approximate state-independent optimization problem in spectral coordinates. The transformed control and disturbance directions are approximated using least-squares spectral projections, and upper and lower bounds on the transformed Hamiltonian are used to derive corresponding bounds on the value function and backward reachable sets. Koopman eigenfunctions are computed using a path-integral-based Galerkin framework that avoids spatial discretization of the associated eigenfunction PDEs. Numerical examples demonstrate tractable approximation of backward reachable sets for nonlinear adversarial systems.

Comparing nonlinear dynamical systems directly from trajectory data remains challenging because finite-horizon trajectory representations are generally coordinate dependent. Motivated by recent behavioral subspace approaches for linear systems, this paper introduces a Koopman-behavioral distance for nonlinear stochastic systems using multi-rollout trajectory data. State measurements are lifted through a common observable dictionary, and dominant lifted behavioral subspaces are compared using Grassmannian distances without explicit Koopman operator identification. We establish coordinate invariance, connection with recently proposed linear behavioral distances under exact lifted closure, and robustness to approximate closure. Numerical examples demonstrate coordinate invariance and parameter discrimination.

Long-duration energy storage (LDES) can mitigate prolonged renewable–load imbalances during Dunkelflaute events, but existing studies rely on zonal or linearized DC network models and inadequately analyze the operational feasibility of the grid across a wide range of full-year renewable, load, and contingency scenarios. To address this gap, this paper introduces a multi-period alternating-current optimal power flow (AC-OPF) formulation that captures the full nonlinear network physics and assesses the reliability value of LDES in high-renewable grids. To evaluate scarcity events spanning multiple days to weeks, full-year operation is modeled as an 8,760-h load-shedding minimization over scenarios sampled from a Gaussian copula model, fitted to 2010–2025 historical wind and bus-level load data, that represents both typical variability and tail events. On a synthetic 200-bus Illinois transmission network, a hybrid fleet of battery energy storage (BESS) and LDES with 50 MW total power reduces annual load shedding by 83.0% on average relative to the base network, versus 68.5% for short-duration BESS alone at equal power. To further account for unexpected line outages throughout the year, the formulation is extended to a multi-day security-constrained AC-OPF. Under N-1contingencies, no feasible operating solution is obtained for the base network, whereas the LDES-equipped network remains feasible in all considered cases, thereby saving the cost of additional generation and transmission capacity. During the contingency period, LDES acts as a backup power supply, requiring only 5.5% more generation on average than the no-contingency base case.

Urban traffic monitoring plays a critical role in safety analysis, congestion management, and incident response. The growing deployment of robotaxis creates a new opportunity for network-level traffic monitoring. Although robotaxis are primarily designed to serve passengers, they can also be leveraged as drive-by sensors to collect traffic data. Compared to conventional infrastructure sensors or probe vehicles, a fleet of robotaxis forms a cooperative perception environment, which can collectively gather spatially and temporally continuous traffic information. This paper proposes a novel dynamic robotaxi routing framework that explicitly incorporates traffic monitoring tasks as an objective. The framework introduces: (1) a cell-based network representation that aligns with sensing capabilities of robotaxis; (2) a cell-level monitoring metric to quantify spatiotemporal robotaxi coverage; and (3) a mixed-integer linear programming (MILP) formulation that jointly minimizes time-dependent travel time and maximizes traffic monitoring performance. A 5 by 5 urban grid network is built in SUMO to evaluate the framework under three robotaxi market penetration rates (2%, 5%, and 10%) with a range of objective weight combinations. Results show that incorporating spatiotemporal network coverage in the objective function can effectively improve the traffic monitoring performance. Interestingly, with appropriate weights between the two objectives, monitoring performance and robotaxi average speed can be improved simultaneously. This suggests better network monitoring leads to more accurate traffic state prediction and improved mobility. This win-win situation could incentivize robotaxi operators to contribute their vehicles as drive-by sensors for traffic monitoring.

AI inference often crosses regional boundaries as prompts travel to remote data centers and generated tokens return to users. Regional averages cannot represent the resulting differences in serving hardware, electricity, and network delivery. Request-level accounting needs a common boundary for the service, serving site, route, local comparator, uncertainty, and data provenance. GreenPassport Carbon Accounting (GPCA) associates these inputs with each request. It estimates serving and route carbon, then selects a reporting level from the available documentation. Our public-data implementation covers data-center instances, accelerators, model families, electricity mixes, routes, and cloud-region carbon intensity. Against six accounting baselines and four energy-prediction baselines, GPCA reduced median absolute percentage error by 56.3% and median absolute error by 15.5% relative to EcoLogits under the aligned accelerator-energy boundary. It produced zero rule overstatement in the deterministic conformance tests. In the buyer case, the clean-electricity CN-West scenario produced 0.0148 gCO2e per request, 88% below the local service at 0.1220 gCO2e per request.

For a class of single-input single-output systems described by proper or improper transfer functions, we propose moment-matching procedures applicable in both continuous- and discrete-time contexts. The resulting technique is not only more flexible and reliable than other procedures which are currently available in literature, but it also enables the placement of constraints on the reduced-order model's poles and zeros. This constraint-based feature, hitherto available only for continuous-time state-space systems, is illustrated via a numerical example based on a practical problem from literature.

The widespread deployment of inverter-based resources (IBRs) is reshaping power system dynamics by their control strategies. Grid-following (GFL) and grid-forming (GFM) inverters are expected to co-exist from now on. However, ensuring the transient stability of such systems remains a critical and underexplored challenge. In this paper, a comprehensive transient stability analysis of the mixed-GFM-GFL inverter system is conducted using a newly derived local energy function. The transient stability analysis model of the system is first derived to reveal the dynamic interactions between GFL and GFM inverters during transient responses. A local energy function is then proposed for analytical transient stability assessment. Instead of ignoring or making harsh assumptions on inverters' coupled damping effect, the proposed local energy function carefully addresses its impact on system transient stability by locally bounding the region where the system's energy dissipation property holds. Such a property fully considers the combined damping effect of GFL and GFM inverters. The derived critical energy and stability index provide a fast and quantitative transient stability assessment. Furthermore, the adverse impact of insufficient damping of GFM inverters on system transient stability is thoroughly investigated. The effectiveness of the proposed method is validated on the modified IEEE 14-bus system with mixed GFL and GFM inverters, and the numerical simulation results further demonstrate the small conservativeness of the proposed method.

For energy-efficient building control, model predictive control (MPC) has been widely proposed to reduce energy use while maintaining prescribed indoor temperature bounds. In practice, MPC implementations may still produce temperature-bound violations because of model mismatch, weather-forecast errors, and softened constraints introduced to preserve feasibility. Existing soft- constrained MPC usually handles this trade-off through slack-weight tuning, so users cannot prescribe a violation-severity budget before operation. This paper develops a Kelvin-hour (Kh)-budget MPC that allows users to specify a running-average budget for temperature-bound violation severity. During operation, the controller uses the realized Kh budget surplus or overspend to update the temperature bounds, providing step-by-step budget feedback. We evaluate the method in two high-fidelity BOPTEST simulation cases and in an occupied residential deployment. In the first one-zone case, prescribed budgets produce clear running-average Kh violation responses across predictors and disturbance settings. At a prescribed budget of 0.005 Kh/step, the controller reduces energy use by 31.9% and Kh violation by 36.2% relative to the built-in default controller. In the second coupled two-zone case, separate zone budgets produce zone-level responses, with 18.9%-20.5% energy reduction and 9.4%-64.3% zone-level Kh violation reduction. In the occupied residential deployment, the running-average Kh violations remain close to or below their prescribed budgets under real sensing, actuation, weather, and occupancy conditions.

Bayes estimation has been extensively studied and widely used in statistics, decision theory, signal processing, machine learning, and system identification. Among its variants, empirical Bayes (EB) estimation has attracted considerable attention due to its favorable estimation performance and computational tractability. However, the direct plug-in dependence of an EB estimator on hyperparameters can make it locally sensitive to hyper-parameter perturbations. This paper considers the linear regression model and focuses on the EB estimator by employing the marginal maximum likelihood hyper-parameter estimator. For conciseness, this estimator is simply referred to as the EB estimator. Given a family of EB weighting functions, a generalized Bayes estimator is constructed with the same excess mean squared error (XMSE) as the corresponding EB estimator. Here, the XMSE is a second-order asymptotic measure of the mean squared error difference between the estimator of interest and the maximum likelihood estimator. Furthermore, the EB estimator is shown to be at most firstorder sensitive to hyper-parameter perturbations, whereas the constructed Bayes estimator is at most second-order sensitive, making it locally more robust. The computational complexities of these two estimators are also analyzed. In some cases, the constructed Bayes estimator can be computationally comparable to, or more efficient than, the EB estimator. These theoretical results are further supported by numerical simulations.

Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load forecasting challenging. Recent advances in time-series foundation models (TSFMs) resulted in promising performance in zero-shot univariate load forecasting tasks. However, real-world load forecasting often involves multiple target variables and requires the integration of exogenous variables, raising important questions about the utility of TSFMs in realistic settings. In this study, we position Chronos-2, a recently developed model by Amazon, as a representative multi-channel TSFM that supports univariate, multivariate, and covariate-informed forecasting, and conduct a systematic investigation of how such models can be used for real-world load forecasting. While prior work has evaluated Chronos-2 on a limited number of energy-related tasks in a zero-shot setting, its performance relative to established task-specific deep learning models and its behavior when adapted using task-specific historical data remains insufficiently understood. In this work, we evaluate Chronos-2 on two real-world utility datasets, ISO New England and ENTSO-E, and benchmark it against widely used task-specific deep learning models. Our results show that Chronos-2 benefits substantially from task-specific fine-tuning and achieves strong short-horizon forecasting performance, but its zero-shot accuracy lags behind task-specific models and its forecasting error grows more rapidly with increasing forecast steps. Overall, this study provides a detailed characterization of the strengths and limitations of TSFMs such as Chronos-2 in grid load forecasting and offers practical insights into how a pretrained TSFM can be effectively adapted for operational load forecasting applications.

Fine-grained mobile traffic demand forecasting is essential for long-term planning of 5G and future 6G networks, including radio upgrades, site densification, backhaul expansion, and spectrum activation. This paper proposes the Traffic Demand Spatio-Temporal Graph Transformer (TD-STGT), a graph neural forecasting framework for predicting changes in wireless mobile traffic demand across fine geographic grids. The framework uses a population-scaled demand proxy developed from crowdsourced mobile measurements and daytime population information. Experiments across five Canadian metropolitan regions show that TD-STGT achieves the best performance in forecasting grid-level demand changes, reaching a $\Delta R^2$ of 0.462 and reducing $\Delta$RMSE by 5.7% relative to the strongest baseline. The proposed model provides a practical tool for identifying areas with increasing demand pressure and prioritizing future mobile-network capacity upgrades.

Reconfigurable, multiplexed power electronic devices (M-PEDs) can provide more effective, flexible operation for applications in active distribution networks, enabling the mitigation of thermal constraints and voltage violations. To-date, operational approaches of these M-PEDs have not been described, a significant issue as a result of their relative complexity as compared to conventional PEDs. This work proposes a state machine-based control philosophy that can be used to manage the mutually exclusive selector- and current-control signals for M-PEDs. Two control approaches are described using this philosophy: an Off-Load approach, suitable for systems without time-critical network constraints; and a Hot-Swap approach, implemented when the impact of transients must be minimized. Simulations illustrate the evolution of the state machine for a back-to-back voltage source converter topology. It is concluded that the proposed state machine-based control philosophy enables M-PED operation whilst managing the additional complexity of this promising converter topology.

Energy-harvesting Internet of Things (IoT) devices must decide whether to transmit each observation immediately or accumulate several observations before reporting to the edge. Early reports make information from initial observations available sooner at the edge but require more transmission actions, whereas larger batches save reports while delaying that information and retaining more state. We develop a unified analytical framework to compare this fixed-batching choice in scenarios with repeated, deadline-constrained inference cycles. Every configuration processes the same ordered observations and yields the same final posterior if all reports arrive, enabling a controlled comparison of the persistent state, actions, and energy costs induced by batching. Using reductions in Bayesian risk under the continuous ranked probability score (CRPS), we express long-run timely value exactly as a statistical-value-weighted sum of expected decision-weighted report availabilities. This separates the inferential value of observations from their availability at application-relevant decision times. We construct exact Markov-reward models accounting jointly for intermittent harvesting, finite storage, energy carry-over, unreliable delivery, retransmissions, and deadlines. We prove that the long-run regime is well defined and derive exact batch-size comparisons, timing benchmarks, and a decomposition of harvesting-law effects into within-cycle and carry-over contributions. Directly evaluable Gaussian and Gaussian-mixture belief specializations show that the preferred batch size can change with the prior, the decision-time profile, link reliability, harvested-energy statistics, and accumulation cost.

This work presents a control framework that enables a multicopter equipped with a two-axis gimbal to emulate the flight dynamics of a fixed-wing aircraft. The goal is to provide an operationally simple platform for training and simulation that avoids the aerodynamic constraints of fixed-wing vehicles, such as minimum airspeed and nonholonomic constraints. A state-input mapping between the two platforms is derived using dynamic feedback linearization. The framework is evaluated on representative fixed-wing manoeuvres. Results show high-fidelity emulation under nominal conditions. While evaluations are conducted in simulation, the approach establishes a practical path toward hardware deployment for pilot training, autonomy research, and controller benchmarking.

Neural inertial odometry has demonstrated strong potential for motion estimation in challenging environments, yet inertial-only preintegration remains sensitive to IMU bias and uncertainty. To this end, this paper introduces PLATO:~Preintegration Learning from Accurate Trajectory Observations, a likelihood-based framework that leverages accurate trajectory observations to jointly learn IMU bias dynamics modeled by a neural ordinary differential equation~(NODE) and gyroscope and accelerometer noise covariances. Optimization exploits the sparse structure of the negative log-likelihood, with IMU noise-parameter gradients computed by forward differentiation. A tailored double-adjoint scheme couples a discrete invariant-error adjoint with a continuous-time adjoint for the bias NODE, enabling memory-efficient likelihood optimization over the nested bias-dynamics and IMU-preintegration rollouts. Validation on EuRoC shows improved performance, and underwater robot experiments demonstrate applicability under intermittent lighting failures and visual degradation.

Networked systems face a tradeoff between the scope of the information behind a decision and its freshness: a broader view of the system supports better coordination, but assembling and communicating it takes time, so it arrives older. We study this tradeoff for a team of agents that repeatedly choose actions to minimize a shared quadratic cost driven by an environment that evolves on its own, unaffected by the agents' actions. An information architecture specifies what each agent observes, from where, and with what delay; we measure an architecture by the cost it loses relative to a decision made with complete, current information. We first compare fresh local observations with a complete but delayed global view. When every component of the environment decorrelates at a common exponential rate, this comparison reduces to a closed-form threshold on the ratio of delay to coherence time. We then study intermediate architectures in which each agent acts on a time-aligned, and therefore older, snapshot of a wider neighborhood. The optimal neighborhood radius occurs where the marginal value of added scope equals the marginal cost of lost freshness. In a canonical spatial model, this radius is set by the spatial-correlation, decision-relevance, and temporal-propagation lengths. Throughout, architecture performance is governed by the predictability of the optimal decision rather than of the raw state.

Robots navigating under occlusion may enter states from which no admissible input can avoid a dynamic obstacle once it becomes visible. We present OcclusionCBF, a safety filter that extends backup control barrier functions to reachable-occupancy predictions for potentially hidden dynamic obstacles in occluded regions. The method certifies a prescribed backup rollout against collision-inflated occupancy and a verified terminal set, yielding affine constraints for minimally invasive quadratic-program filtering. We establish recursive feasibility of the resulting safety filter, and collision avoidance for every hidden-obstacle motion covered by the occupancy prediction. Randomized benchmarks, MetaUrban simulations, and hardware experiments demonstrate improved task success over reactive and occlusion-aware predictive baselines with millisecond-scale computation.

Real-time control often sits between two limiting regimes. Predictive optimization and model-based control are powerful when dynamics, parameters, objectives, and online planning models are specified; reinforcement learning can relax this requirement, but must infer long-horizon value signals from sequential data and interaction, making training slow, high-variance, and hard to scale in large action spaces. This middle regime is common in systems including autonomous driving, warehouse robotics, traffic control, and delivery drones: partial geometry, physics, rules, or constraints are known, yet the local direction of task progress remains uncertain. Geometric Distributional Control (GDC) is designed for this partial-knowledge setting. It factorizes control into feasibility and progress: known geometry, rules, constraints, and response maps define an executable scaffold, while progress-weighted feasible data learns the missing directional signal on that scaffold. The learned score acts as a Bellman-like local value-gradient, selecting actions that make progress without requiring global Bellman recursion, a fully specified planner, or a black-box policy that absorbs both feasibility and preference. This knowledge can be lightweight and partial, such as simple dynamics, safety filters, local maps, constraint projectors, or lower-level response maps; it need not encode full dynamics or a long-horizon objective. Offline, GDC fits a progress-tilted distribution from short known-feasible snippets with weak signed progress certificates. Online, its score is projected through the scaffold and applied in receding-horizon feedback. We prove that this score descends a data-induced soft progress value and validate GDC on structured multilevel optimization and SUMO route-progress driving, where it improves over known-only solvers and learning baselines while preserving scaffold-enforced feasibility.

Generalist robots promise to transform our society: the same system that prepares a meal or folds laundry might also repair a car, inspect infrastructure, or care for a loved one. Yet this versatility introduces risks far beyond the collision- and force-based safety notions that have long dominated robotics. Notions of safety must now consider context (e.g., turning off a building's electricity is only safe during scheduled maintenance), user intent (e.g., asking the robot to “clean the kitchen” includes unspoken expectations that the robot should not mix dangerous but powerful cleaning agents like bleach and ammonia), hard-to-model physical consequences (e.g., burning food during meal preparation), and more. We argue the need for a new era of robot safety—embodied AI safety—that broadens the hazards considered across the robot's lifecycle while recognizing that the safety of bits cannot be separated from the safety of atoms. We present a taxonomy of emerging risks and a full-stack research agenda to guide the safe deployment of generalist robots.