3D point cloud classification is a fundamental task in safety-critical applications such as autonomous driving, robotics, and augmented reality. However, recent studies reveal that point cloud classifiers are vulnerable to structured adversarial perturbations and geometric corruptions, posing risks to their deployment in safety-critical scenarios. Existing certified defenses limit point-wise perturbations but overlook subtle geometric distortions that preserve individual points yet alter the overall structure, potentially leading to misclassification. In this work, we propose FreqCert, a novel certification framework that departs from conventional spatial domain defenses by shifting robustness analysis to the frequency domain, enabling structured certification against global l2-bounded perturbations. FreqCert first transforms the input point cloud via the graph Fourier transform (GFT), then applies structured frequency-aware subsampling to generate multiple sub-point clouds. Each sub-cloud is independently classified by a standard model, and the final prediction is obtained through majority voting, where sub-clouds are constructed based on spectral similarity rather than spatial proximity, making the partitioning more stable under l2 perturbations and better aligned with the object’s intrinsic structure. We derive a closed-form lower bound on the certified l2 robustness radius and prove its tightness under minimal and interpretable assumptions, establishing a theoretical foundation for frequency domain certification. Extensive experiments on the ModelNet40 and ScanObjectNN datasets demonstrate that FreqCert consistently achieves higher certified accuracy and empirical accuracy under strong perturbations. Our results suggest that spectral representations provide an effective pathway toward certifiable robustness in 3D point cloud recognition.

This paper proposes 12 multi-object grasps (MOGs) types from a human and robot grasping data set. The grasp types are then analyzed and organized into a MOG taxonomy. This paper first presents three MOG data collection setups: a human finger tracking setup for multi-object grasping demonstrations, a real system with Barretthand, UR5e arm, and a MOG algorithm, a simulation system with the same settings as the real system. Then the paper describes a novel stochastic grasping routine designed based on a biased random walk to explore the robotic hand's configuration space for feasible MOGs. Based on obser-vations in both the human demonstrations and robotic MOG solutions, this paper proposes 12 MOG types in two groups: shape-based types and function-based types. The new MOG types are compared using six characteristics and then compiled into a taxonomy. This paper then introduces the observed MOG type combinations and shows examples of 16 different combinations.

In a typical fulfillment center, the order fulfilling process is managed by a warehouse management system (WMS). For efficiency, WMS usually applies batch picking, also called multi-order picking, to collect the same items for multiple orders. Suppose an item appears in multiple orders, instead of repeatedly revisiting the exact picking location multiple times, a picker will be instructed to pick up multiple same items at once and bring them to a sorting station, also called a re-bin station. It is at the re-bin station, where the workers sort the picked items into separate orders. We have seen many robotic technologies being developed for sorting. However, we have not seen any feasible robotic technology for batch picking. Transferring multiple objects between bins is a common task. In robotics, a standard approach is to transfer a single object at a time. However, grasping multiple objects and transferring them at once is more efficient. This paper presents a set of novel strategies for efficiently grasping and transferring multiple objects. The grasping strategies enable a robotic hand to grasp multiple objects by identifying an optimal ready hand configuration (pre-grasp), calculating a flexion synergy based on the desired quantity of objects to be grasped, and utilizing a deep learning model to signal the completion of a grasp. The transferring strategies demonstrate an approach that models the problem as a Markov decision process (MDP) and defines specific grasping actions to efficiently transfer objects when the required quantity is larger than the capability of a single grasp. Using the MDP model, the approach can generate an optimal pick-transfer policy that minimizes the number of transfers. The complete proposed approach has been evaluated in both a simulation environment and on a real robotic system. The proposed approach reduces the number of transfers by 59% and the number of lifts by 58% compared to an optimal single object pick-transfer solution.

A human hand can grasp a desired number of objects at once from a pile based solely on tactile sensing. To do so, a robot needs to make a grasp in a pile, sense the number of objects in the grasp before lifting, and predict how many will remain in the grasp after lifting. It is a very challenging problem because when making the prediction, the robotic hand is still in the pile and the objects in the grasp are not observable to vision systems. Moreover, some objects in the hand before lifting may fall out the grasp when the lifting starts because they were supported by other objects in the pile instead of the fingers. A robotic hand should sense how many objects are in a grasp using its tactile sensors before lifting. This paper presents novel multi-object grasping analyzing methods to solve this problem. They include a grasp volume calculation, tactile force analysis, and a data-driven deep learning approach. The methods have been implemented on a Barrett hand and then evaluated in simulations and a real setup with a robotic system. The evaluation results conclude that once the Barrett hand grasps multiple objects in the pile, the data-driven models can make a good prediction before lifting on how many objects will remain in the hand after lifting. The root-mean-square errors are 0.74 for balls and 0.58 for cubes in simulations, and 1.06 for balls and 1.45 for cubes in the real system.

Humans perform the task of pouring often and in which exhibit consistent accuracy regardless of the complicated dynamics of the liquid. Model predictive control (MPC) appears to be a natural candidate solution for the task of accurate pouring considering its wide use in industrial applications. However, MPC requires the model of the system in question. Since an accurate model of the liquid dynamics is difficult to obtain, the usefulness of MPC for the pouring task is uncertain. In this work, we model the dynamics of water using a recurrent neural network (RNN), which enables the use of MPC for pouring control. We evaluated our RNN-enabled MPC controller using a physical system we made ourselves and averaged a pouring error of 16.4 mL over 5 different source containers. We also compared our controller with a baseline switch controller and showed that our controller achieved a much higher accuracy than the baseline controller.

In this letter, a synthetic aperture radar (SAR) image registration method based on edge-point features is presented to improve precision, robustness, and applicability of SAR image registration. First, an affine transformation model is decomposed into six parameters with explicit geometric meanings. Next, based on the strength and direction features of the edge point, a matching similarity criterion and a joint similarity metric, i.e., the square summation joint feature, are constructed. Then, the parameters of the transformation model between SAR images are solved with a modified genetic algorithm that is able to get a global optimal solution of the metric. Finally, the performance of the proposed method is validated with two SAR image registration experiments.