Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Endocan, a Risk Factor for Developing Acute Respiratory Distress Syndrome among Severe Pneumonia Patients

Background. Severe pneumonia (SP) has been widely accepted as a major cause for acute respiratory distress syndrome (ARDS), and the development of ARDS is significantly associated with increased mortality. This study aimed to identify potential predictors for ARDS development in patients with SP. Methods. Eligible SP patients at admission from January 2013 to June 2017 were prospectively enrolled, and ARDS development within hospital stay was identified. Risk factors for ARDS development in SP patients were analyzed by univariate and multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve analysis with the area under the curve (AUC) was performed for the predictive value of endocan for ARDS development. Results. A total of 145 SP patients were eventually enrolled into the final analysis, of which 37 developed ARDS during the hospital stay. Our final multivariate logistic regression analysis suggested plasma endocan expression as the only independent risk factor for ARDS development in SP patients (OR: 1.57, 95% CI: 1.14–2.25, P=0.021). ROC curve analysis of plasma endocan resulted in an AUC of 0.754, 95% CI of 0.642–0.866, a cutoff value of 11.6 ng/mL, a sensitivity of 78.7%, and a specificity of 70.3%, respectively (P<0.01). Conclusions. Endocan expression at ICU admission is a reliable predictive factor in predicting ARDS in patients with SP

Pretreatment albumin/fibrinogen ratio as a promising predictor for the survival of advanced non small-cell lung cancer patients undergoing first-line platinum-based chemotherapy

Abstract Background This study aimed to identify potential predictive factors for the survival of advanced non small-cell lung cancer (NSCLC) patients undergoing first-line platinum-based chemotherapy. Methods A total of 270 advanced NSCLC patients who underwent first-line platinum-based chemotherapy from June, 2011 to June, 2015 were enrolled. A receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of the albumin-to-fibrinogen ratio (AFR) for overall survival (OS). The predictive factors for survival were evaluated by univariate and multivariate analyses via the Cox proportional hazards regression model. The OS and progression free survival (PFS) results were determined via the Kaplan–Meier method using the log-rank analysis. Results Based on the results of the ROC curve analysis, 8.02 was accepted as the cut-off AFR value for OS. The metastasis stage (M0 vs M1a/b, HR: 1.73, 95% CI: 1.15–2.59, P = 0.020) and AFR (≤8.02 vs > 8.02, HR: 1.80, 95% CI: 1.09–2.78, P = 0.025) were two independent risk factors for PFS by multivariate Cox regression analysis. The AFR (≤8.02 vs > 8.02, HR: 1.79, 95% CI: 1.11–2.59, P = 0.029) was a significant predictive factor for OS in advanced NSCLC patients. The PFS (P = 0.008) and OS (P = 0.003) in the high AFR group were significantly improved compared with those in the low AFR group via the Kaplan–Meier method using the log-rank analysis. Conclusions The AFR could be a potential effective predictive factor for the survival in advanced NSCLC patients undergoing first-line platinum-based chemotherapy

Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist"X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io.</p