Online Robot Introspection via Wrench-based Action Grammars

Robotic failure is all too common in unstructured robot tasks. Despite well-designed controllers, robots often fail due to unexpected events. How do robots measure unexpected events? Many do not. Most robots are driven by the sense-plan act paradigm, however more recently robots are undergoing a sense-plan-act-verify paradigm. In this work, we present a principled methodology to bootstrap online robot introspection for contact tasks. In effect, we are trying to enable the robot to answer the question: what did I do? Is my behavior as expected or not? To this end, we analyze noisy wrench data and postulate that the latter inherently contains patterns that can be effectively represented by a vocabulary. The vocabulary is generated by segmenting and encoding the data. When the wrench information represents a sequence of sub-tasks, we can think of the vocabulary forming a sentence (set of words with grammar rules) for a given sub-task; allowing the latter to be uniquely represented. The grammar, which can also include unexpected events, was classified in offline and online scenarios as well as for simulated and real robot experiments. Multiclass Support Vector Machines (SVMs) were used offline, while online probabilistic SVMs were are used to give temporal confidence to the introspection result. The contribution of our work is the presentation of a generalizable online semantic scheme that enables a robot to understand its high-level state whether nominal or abnormal. It is shown to work in offline and online scenarios for a particularly challenging contact task: snap assemblies. We perform the snap assembly in one-arm simulated and real one-arm experiments and a simulated two-arm experiment. This verification mechanism can be used by high-level planners or reasoning systems to enable intelligent failure recovery or determine the next most optima manipulation skill to be used.Comment: arXiv admin note: substantial text overlap with arXiv:1609.0494

Test-Time Training for Semantic Segmentation with Output Contrastive Loss

Although deep learning-based segmentation models have achieved impressive performance on public benchmarks, generalizing well to unseen environments remains a major challenge. To improve the model's generalization ability to the new domain during evaluation, the test-time training (TTT) is a challenging paradigm that adapts the source-pretrained model in an online fashion. Early efforts on TTT mainly focus on the image classification task. Directly extending these methods to semantic segmentation easily experiences unstable adaption due to segmentation's inherent characteristics, such as extreme class imbalance and complex decision spaces. To stabilize the adaptation process, we introduce contrastive loss (CL), known for its capability to learn robust and generalized representations. Nevertheless, the traditional CL operates in the representation space and cannot directly enhance predictions. In this paper, we resolve this limitation by adapting the CL to the output space, employing a high temperature, and simplifying the formulation, resulting in a straightforward yet effective loss function called Output Contrastive Loss (OCL). Our comprehensive experiments validate the efficacy of our approach across diverse evaluation scenarios. Notably, our method excels even when applied to models initially pre-trained using domain adaptation methods on test domain data, showcasing its resilience and adaptability.\footnote{Code and more information could be found at~ \url{https://github.com/dazhangyu123/OCL}

Impact of chest pain center quality control indicators on mortality risk in ST-segment elevation myocardial infarction patients: a study based on Killip classification

BackgroundDespite the crucial role of Chest pain centers (CPCs) in acute myocardial infarction (AMI) management, China's mortality rate for ST-segment elevation myocardial infarction (STEMI) has remained stagnant. This study evaluates the influence of CPC quality control indicators on mortality risk in STEMI patients receiving primary percutaneous coronary intervention (PPCI) during the COVID-19 pandemic.MethodsA cohort of 664 consecutive STEMI patients undergoing PPCI from 2020 to 2022 was analyzed using Cox proportional hazards regression models. The cohort was stratified by Killip classification at admission (Class 1: n = 402, Class ≥2: n = 262).ResultsAt a median follow-up of 17 months, 35 deaths were recorded. In Class ≥2, longer door-to-balloon (D-to-B) time, PCI informed consent time, catheterization laboratory activation time, and diagnosis-to-loading dose dual antiplatelet therapy (DAPT) time were associated with increased mortality risk. In Class 1, consultation time (notice to arrival) under 10 min reduced death risk. In Class ≥2, PCI informed consent time under 20 min decreased mortality risk.ConclusionCPC quality control metrics affect STEMI mortality based on Killip class. Key factors include time indicators and standardization of CPC management. The study provides guidance for quality care during COVID-19

Interferometry Using Generalized Lock-in Amplifier (G-LIA): A Versatile Approach for Phase-Sensitive Sensing and Imaging

A large number of interferometric setups make use of non-linear phase modulators. In the past, specific extraction methods have been proposed mostly to cover the important case of sinusoidal phase modulation with certain limits in term of signal-to-noise ratio. Recently, a detection method based on “Generalized Lock-in Amplifier” (G-LIA) was proposed to extract optimally amplitude and phase information in two-arm interferometers when nearly arbitrary phase modulations are used such as triangular or sinusoidal phase modulations. This method offers the opportunity to develop highly sensitive interferometers with simple-phase modulators such as piezo-actuated mirrors, piezo stretchers, or power-modulated laser diodes in unbalanced interferometers. Here we present the basics of the approach and we give application examples for monitoring displacement, sensing, and digital holography. The case where an amplitude modulation is also present is also detailed and discussed in the context of unbalanced interferometry and near-field nanoscopy