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This work develops a data-efficient learning from demonstration framework which exploits the use of rich tactile sensing and achieves fine dexterous bimanual manipulation. Specifically, we formulated a convolutional autoencoder network that can effectively extract and encode high-dimensional tactile information. Further, we developed a behaviour cloning network that can learn human-like sensorimotor skills demonstrated directly on the robot hardware in the task space by fusing both proprioceptive and tactile feedback. Our comparison study with the baseline method revealed the effectiveness of the contact information, which enabled successful extraction and replication of the demonstrated motor skills. Extensive experiments on real dual-arm robots demonstrated the robustness and effectiveness of the fine pinch grasp policy directly learned from one-shot demonstration, including grasping of the same object with different initial poses, generalizing to ten unseen new objects, robust and firm grasping against external pushes, as well as contact-aware and reactive re-grasping in case of dropping objects under very large perturbations. Moreover, the saliency map method is employed to describe the weight distribution across various modalities during pinch grasping. The video is available online at: \href{//youtu.be/4Pg29bUBKqs}{//youtu.be/4Pg29bUBKqs}.

The Global Precedence Effect (GPE) suggests that the processing of global properties of a visual stimulus precedes the processing of local properties. The generality of this theory was argued for four decades during different known Perceptual Field Variables. The effect size of various PFVs, regarding the findings during these four decades, were pooled in our recent meta-analysis study. Pursuing the study, in the present paper, we explore the effects of Congruency, Size, and Sparsity and their interaction on global advantage in two different experiments with different task paradigms; Matching judgment and Similarity judgment. Upon results of these experiments, Congruency and Size have significant effects and Sparsity has small effects. Also, the task paradigm and its interaction with other PFVs are shown significant effects in this study, which shows the prominence of the role of task paradigms in evaluating PFVs' effects on GPE. Also, we found that the effects of these parameters were not specific to the special condition that individuals were instructed to retinal stabilize. So, the experiments were more extendible to daily human behavior.

The ability to detect slip, particularly incipient slip, enables robotic systems to take corrective measures to prevent a grasped object from being dropped. Therefore, slip detection can enhance the overall security of robotic gripping. However, accurately detecting incipient slip remains a significant challenge. In this paper, we propose a novel learning-based approach to detect incipient slip using the PapillArray (Contactile, Australia) tactile sensor. The resulting model is highly effective in identifying patterns associated with incipient slip, achieving a detection success rate of 95.6% when tested with an offline dataset. Furthermore, we introduce several data augmentation methods to enhance the robustness of our model. When transferring the trained model to a robotic gripping environment distinct from where the training data was collected, our model maintained robust performance, with a success rate of 96.8%, providing timely feedback for stabilizing several practical gripping tasks. Our project website: //sites.google.com/view/incipient-slip-detection.

Community detection is a classic problem in network science with extensive applications in various fields. Among numerous approaches, the most common method is modularity maximization. Despite their design philosophy and wide adoption, heuristic modularity maximization algorithms rarely return an optimal partition or anything similar. We propose a specialized algorithm, Bayan, which returns partitions with a guarantee of either optimality or proximity to an optimal partition. At the core of the Bayan algorithm is a branch-and-cut scheme that solves an integer programming formulation of the modularity maximization problem to optimality or approximate it within a factor. We compare Bayan against 30 alternative community detection methods using structurally diverse synthetic and real networks. Our results demonstrate Bayan's distinctive accuracy and stability in retrieving ground-truth communities of standard benchmark graphs. Bayan is several times faster than open-source and commercial solvers for modularity maximization making it capable of finding optimal partitions for instances that cannot be optimized by any other existing method. Overall, our assessments point to Bayan as a suitable choice for exact maximization of modularity in real networks with up to 3000 edges (in their largest connected component) and approximating maximum modularity in larger instances on ordinary computers. A Python implementation of the Bayan algorithm (the bayanpy library) is publicly available through the package installer for Python (pip).

The big crux with drug delivery to human lungs is that the delivered dose at the local site of action is unpredictable and very difficult to measure, even a posteriori. It is highly subject-specific as it depends on lung morphology, disease, breathing, and aerosol characteristics. Given these challenges, computational approaches have shown potential, but have so far failed due to fundamental methodical limitations. We present and validate a novel in silico model that enables the subject-specific prediction of local aerosol deposition throughout the entire lung. Its unprecedented spatiotemporal resolution allows to track each aerosol particle anytime during the breathing cycle, anywhere in the complete system of conducting airways and the alveolar region. Predictions are shown to be in excellent agreement with in vivo SPECT/CT data for a healthy human cohort. We further showcase the model's capabilities to represent strong heterogeneities in diseased lungs by studying an IPF patient. Finally, high computational efficiency and automated model generation and calibration ensure readiness to be applied at scale. We envision our method not only to improve inhalation therapies by informing and accelerating all stages of (pre-)clinical drug and device development, but also as a more-than-equivalent alternative to nuclear imaging of the lungs.

Choosing how to encode a real-world problem as a machine learning task is an important design decision in machine learning. The task of glacier calving front modeling has often been approached as a semantic segmentation task. Recent studies have shown that combining segmentation with edge detection can improve the accuracy of calving front detectors. Building on this observation, we completely rephrase the task as a contour tracing problem and propose a model for explicit contour detection that does not incorporate any dense predictions as intermediate steps. The proposed approach, called ``Charting Outlines by Recurrent Adaptation'' (COBRA), combines Convolutional Neural Networks (CNNs) for feature extraction and active contour models for the delineation. By training and evaluating on several large-scale datasets of Greenland's outlet glaciers, we show that this approach indeed outperforms the aforementioned methods based on segmentation and edge-detection. Finally, we demonstrate that explicit contour detection has benefits over pixel-wise methods when quantifying the models' prediction uncertainties. The project page containing the code and animated model predictions can be found at \url{//khdlr.github.io/COBRA/}.

Vehicle-to-Vehicle (V2V) communication is intended to improve road safety through distributed information sharing; however, this type of system faces a design challenge: it is difficult to predict and optimize how human agents will respond to the introduction of this information. Bayesian games are a standard approach for modeling such scenarios; in a Bayesian game, agents probabilistically adopt various types on the basis of a fixed, known distribution. Agents in such models ostensibly perform Bayesian inference, which may not be a reasonable cognitive demand for most humans. To complicate matters, the information provided to agents is often implicitly dependent on agent behavior, meaning that the distribution of agent types is a function of the behavior of agents (i.e., the type distribution is endogenous). In this paper, we study an existing model of V2V communication, but relax it along two dimensions: first, we pose a behavior model which does not require human agents to perform Bayesian inference; second, we pose an equilibrium model which avoids the challenging endogenous recursion. Surprisingly, we show that the simplified non-Bayesian behavior model yields the exact same equilibrium behavior as the original Bayesian model, which may lend credibility to Bayesian models. However, we also show that the original endogenous equilibrium model is strictly necessary to obtain certain informational paradoxes; these paradoxes do not appear in the simpler exogenous model. This suggests that standard Bayesian game models with fixed type distributions are not sufficient to express certain important phenomena.

The ability to accurately locate and navigate to a specific object is a crucial capability for embodied agents that operate in the real world and interact with objects to complete tasks. Such object navigation tasks usually require large-scale training in visual environments with labeled objects, which generalizes poorly to novel objects in unknown environments. In this work, we present a novel zero-shot object navigation method, Exploration with Soft Commonsense constraints (ESC), that transfers commonsense knowledge in pre-trained models to open-world object navigation without any navigation experience nor any other training on the visual environments. First, ESC leverages a pre-trained vision and language model for open-world prompt-based grounding and a pre-trained commonsense language model for room and object reasoning. Then ESC converts commonsense knowledge into navigation actions by modeling it as soft logic predicates for efficient exploration. Extensive experiments on MP3D, HM3D, and RoboTHOR benchmarks show that our ESC method improves significantly over baselines, and achieves new state-of-the-art results for zero-shot object navigation (e.g., 288% relative Success Rate improvement than CoW on MP3D).

When is heterogeneity in the composition of an autonomous robotic team beneficial and when is it detrimental? We investigate and answer this question in the context of a minimally viable model that examines the role of heterogeneous speeds in perimeter defense problems, where defenders share a total allocated speed budget. We consider two distinct problem settings and develop strategies based on dynamic programming and on local interaction rules. We present a theoretical analysis of both approaches and our results are extensively validated using simulations. Interestingly, our results demonstrate that the viability of heterogeneous teams depends on the amount of information available to the defenders. Moreover, our results suggest a universality property: across a wide range of problem parameters the optimal ratio of the speeds of the defenders remains nearly constant.

In recent years, object detection has experienced impressive progress. Despite these improvements, there is still a significant gap in the performance between the detection of small and large objects. We analyze the current state-of-the-art model, Mask-RCNN, on a challenging dataset, MS COCO. We show that the overlap between small ground-truth objects and the predicted anchors is much lower than the expected IoU threshold. We conjecture this is due to two factors; (1) only a few images are containing small objects, and (2) small objects do not appear enough even within each image containing them. We thus propose to oversample those images with small objects and augment each of those images by copy-pasting small objects many times. It allows us to trade off the quality of the detector on large objects with that on small objects. We evaluate different pasting augmentation strategies, and ultimately, we achieve 9.7\% relative improvement on the instance segmentation and 7.1\% on the object detection of small objects, compared to the current state of the art method on MS COCO.