To fill this gap, we introduce a nonparametric causal inference framework for analyzing "closed-loop" designs, which use

Neural Information Processing Systems http://nips.cc/

To fill this gap, we introduce a nonparametric causal inference framework for analyzing "closed-loop" designs, which use

This paper extends first-order motion planners to robots governed by second-order dynamics. Two control schemes are proposed based on the knowledge of a scalar function whose negative gradient aligns with a given first-order motion planner. When such a function is known, the first-order motion planner is combined with a damping velocity vector with a dynamic gain to extend the safety and convergence guarantees of the first-order motion planner to second-order systems. If no such function is available, we propose an alternative control scheme ensuring that the error between the robot's velocity and the first-order motion planner converges to zero. The theoretical developments are supported by simulation results demonstrating the effectiveness of the proposed approaches.

To unlock access to stronger winds, the offshore wind industry is advancing with significantly larger and taller wind turbines. This massive upscaling motivates a departure from univariate wind forecasting methods that traditionally focused on a single representative height. To fill this gap, we propose DeepMIDE--a statistical deep learning method which jointly models the offshore wind speeds across space, time, and height. DeepMIDE is formulated as a multi-output integro-difference equation model with a multivariate, nonstationary, and state-dependent kernel characterized by a set of advection vectors that encode the physics of wind field formation and propagation. Embedded within DeepMIDE, an advanced deep learning architecture learns these advection vectors from high dimensional streams of exogenous weather information, which, along with other parameters, are plugged back into the statistical model for probabilistic multi-height space-time forecasting. Tested on real-world data from future offshore wind energy sites in the Northeastern United States, the wind speed and power forecasts from DeepMIDE are shown to outperform those from prevalent time series, spatio-temporal, and deep learning methods.

Deep neural networks (DNNs) have shown remarkable performance in a wide range of tasks over the past decade (Bengio et al. 2021). A mystery is that they generalize surprisingly well on unseen data, though having far more trainable parameters than the number of training examples (Belkin et al. 2019, Li et al. 2023). This phenomenon of benign overfitting inevitably casts shadows on the classical theory of statistical learning, which posits that models with high complexity tend to overfit the training data, whereas models with low complexity tend to underfit the training data. To reconcile the conflicts, some researchers argue that this is due to the regularization incurred during training, either implicitly imposed via use of stochastic gradient descent (SGD) (Advani et al. 2020, Barrett & Dherin 2021, Smith et al. 2021, Sclocchi & Wyart 2024) or explicitly via batch normalization (Ioffe & Szegedy 2015), weight decay (Krogh & Hertz 1992), dropout (Srivastava et al. 2014), etc. However, Zhang et al. (2017) questioned this widely received wisdom because they found that DNNs are still able to achieve zero training error with randomly labeled examples, which apparently cannot generalize. Prior to our work, there has been extensive study trying to explain the generalization behavior of DNNs and they roughly can be categorized into the following classes. The first class is the so-called norm-based bounds (Neyshabur et al. 2015, Bartlett et al. 2017, Neyshabur et al. 2018, Golowich et al. 2018) that are composed of the operator norm of layerwise weight matrices. However, recent studies suggest that these norm-based bounds might be problematic as they abnormally increase with the number of training examples (Nagarajan & Kolter 2019). Moreover, norm-based bounds are numerically vacuous as they are even several orders of magnitude larger than the number of network parameters.

This paper introduces the notion of a universal plan, which when executed, is guaranteed to solve all planning problems in a category, regardless of the obstacles, initial state, and goal set. Such plans are specified as a deterministic sequence of actions that are blindly applied without any sensor feedback. Thus, they can be considered as pure exploration in a reinforcement learning context, and we show that with basic memory requirements, they even yield asymptotically optimal plans. Building upon results in number theory and theory of automata, we provide universal plans both for discrete and continuous (motion) planning and prove their (semi)completeness. The concepts are applied and illustrated through simulation studies, and several directions for future research are sketched.

We propose a hybrid feedback control scheme for the autonomous robot navigation problem in three-dimensional environments with arbitrarily-shaped convex obstacles. The proposed hybrid control strategy, which consists in switching between the move-to-target mode and the obstacle-avoidance mode, guarantees global asymptotic stability of the target location in the obstacle-free workspace. We also provide a procedure for the implementation of the proposed hybrid controller in a priori unknown environments and validate its effectiveness through simulation results.

We develop an autonomous navigation algorithm for a robot operating in two-dimensional environments containing obstacles, with arbitrary non-convex shapes, which can be in close proximity with each other, as long as there exists at least one safe path connecting the initial and the target location. An instrumental transformation that modifies (virtually) the non-convex obstacles, in a non-conservative manner, is introduced to facilitate the design of the obstacle-avoidance strategy. The proposed navigation approach relies on a hybrid feedback that guarantees global asymptotic stabilization of a target location while ensuring the forward invariance of the modified obstacle-free workspace. The proposed hybrid feedback controller guarantees Zeno-free switching between the move-to-target mode and the obstacle-avoidance mode based on the proximity of the robot with respect to the modified obstacle-occupied workspace. Finally, we provide an algorithmic procedure for the sensor-based implementation of the proposed hybrid controller and validate its effectiveness via some numerical simulations.

To this end, vast amounts of data generated by various sources, including sensors and other monitoring systems, need to be effectively structured and represented in a way that can be easily understood and processed by both Artificial Intelligence (AI) systems and humans. The digitalisation of the wind energy sector is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle [2]. The digitalisation process encompasses solutions such as digital twins, decision support systems and AI systems, some of which need to still be developed, in order to contribute to reducing operation and maintenance costs, for increasing the amount of energy delivered, as well as for maximising the efficiency of wind energy systems. In this context, the term Knowledge-Based Systems (KBS) refers to AI systems that formalize knowledge as rules, logical expressions, and conceptualisations [3, 4]. Such systems can be realised as AI-enabled digital twins or decision support systems that rely on databases of knowledge (also referred to as knowledge bases or knowledge graphs), which contain machine-readable facts, rules, and logics about a domain of interest, to assist with problem-solving and decision-making [5].