This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses a categoryagnostic affordance prediction algorithm to select among four different grasping primitive behaviors. It then recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional training data. Exhaustive experimental results demonstrate that our multi-affordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at http://arc.cs.princeton.edu

This paper presents a robotic pick-and-place system that is capable of grasping and recognizing both known and novel objects in cluttered environments. The key new feature of the system is that it handles a wide range of object categories without needing any task-specific training data for novel objects. To achieve this, it first uses an object-agnostic grasping framework to map from visual observations to actions: inferring dense pixel-wise probability maps of the affordances for four different grasping primitive actions. It then executes the action with the highest affordance and recognizes picked objects with a cross-domain image classification framework that matches observed images to product images. Since product images are readily available for a wide range of objects (e.g., from the web), the system works out-of-the-box for novel objects without requiring any additional data collection or re-training. Exhaustive experimental results demonstrate that our multiaffordance grasping achieves high success rates for a wide variety of objects in clutter, and our recognition algorithm achieves high accuracy for both known and novel grasped objects. The approach was part of the MIT-Princeton Team system that took 1st place in the stowing task at the 2017 Amazon Robotics Challenge. All code, datasets, and pre-trained models are available online at

No abstract

Background Cardiovascular disease (CVD) risk prediction is important in guiding the intensity of therapy in CVD prevention. Whilst current risk prediction algorithms use traditional statistical approaches, machine learning (ML) presents an alternative method that may improve risk prediction accuracy. This systematic review and meta-analysis aimed to investigate whether ML algorithms demonstrate greater performance compared to traditional risk scores in CVD risk prognostication. Methods MEDLINE, EMBASE, CENTRAL and SCOPUS Web of Science Core collection were searched for studies comparing ML models to traditional risk scores for CV risk prediction between the years 2000 and 2021. We included studies which assessed both ML and traditional risk scores in adult (>18 years old) primary prevention populations. We assessed risk of bias using the Prediction model Risk of Bias Assessment Tool (PROBAST) tool. Only studies which provided a measure of discrimination (i.e. C-statistics with 95% confidence intervals) were included in the meta-analysis. Results Sixteen studies were included in the review and meta-analysis (3 302 515 individuals). All study designs were retrospective cohort studies. Three of 16 studies externally validated their models, and 11 reported calibration metrics. Eleven studies demonstrated a high risk of bias. The summary c-statistics (95% CI) of the top performing ML models and traditional risk scores were 0.773 (95%CI: 0.740—0.806) and 0.759 (95%CI: 0.726—0.792) respectively. The difference in c-statistic was 0.0139 (95%CI 0.0139—0.140), P < 0.0001. Conclusion ML models outperformed traditional risk scores in discrimination of CVD risk prognostication. Integration of ML algorithms into electronic healthcare systems in primary care could improve identification of patients at high risk of subsequent CV events and hence increase opportunities for CVD prevention. It is uncertain whether they can be implemented in clinical settings. Future implementation research is needed to examine how ML models may be utilised for primary prevention. This review was registered with PROSPERO (CRD42020220811).