REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction

The ability to detect and analyze failed executions automatically is crucial for an explainable and robust robotic system. Recently, Large Language Models (LLMs) have demonstrated strong common sense reasoning skills on textual inputs. To leverage the power of LLM for robot failure explanation, we propose a framework REFLECT, which converts multi-sensory data into a hierarchical summary of robot past experiences and queries LLM with a progressive failure explanation algorithm. Conditioned on the explanation, a failure correction planner generates an executable plan for the robot to correct the failure and complete the task. To systematically evaluate the framework, we create the RoboFail dataset and show that our LLM-based framework is able to generate informative failure explanations that assist successful correction planning. Project website: https://roboreflect.github.io

Analyses and Discussions of the Blackout in Indian Power Grid

Widespread blackouts of “7.30” and “7.31” occurred in India in 2012. Severe problems in the infrastructure, operation and management in India power grid have come to light after the successive blackouts. The fundamental reason and trigger factors are analyzed in this paper first. Based on the similar characteristics between China power grid and India power grid, in the case of Chinese grid, lessons of preventing blackout of large power grid were discussed by analysis and comparison, mainly on harmonious development and interactive operation of source-grid-load, and construction of wide-area protection system. Suggestions are proposed to improve the security and stability of large power grid with coordinated control system

The Noninvasive Measurement of Central Aortic Blood Pressure Waveform

Central aortic pressure (CAP) is a potential surrogate of brachial blood pressure in both clinical practice and routine health screening. It directly reflects the status of the central aorta. Noninvasive measurement of CAP becomes a crucial technique of great interest. There have been advances in recent years, including the proposal of novel methods and commercialization of several instruments. This chapter briefly introduces the clinical importance of CAP and the theoretical basis for the generation of CAP in the first and second sections. The third section describes and discusses the measurement of peripheral blood pressure waveforms, which is employed to estimate CAP. We then review the proposed methods for the measurement of CAP. The calibration of blood pressure waveforms is discussed in the fourth section. After a brief discussion of the technical limitations, we give suggestions for perspectives and future challenges

Robotic Pick-and-Place of Novel Objects in Clutter with Multi-Affordance Grasping and Cross-Domain Image Matching

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 category-agnostic affordance prediction algorithm to select and execute 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.eduComment: Project webpage: http://arc.cs.princeton.edu Summary video: https://youtu.be/6fG7zwGfIk

Identification and Validation of METTL3-Related Molecules for Predicting Prognosis and Efficacy of Immunotherapy in Gastric Cancer Based on m6A Methylome and Transcriptome Sequencing Analysis

Abnormal N6-methyladenosine (m6A) modification levels caused by METTL3 have been identified to be a critical regulator in human cancers, and its roles in the immune microenvironment and the relationship between targeted therapy and immunotherapy sensitivity in gastric cancer (GC) remain poorly understood. In this study, we assessed the transcriptome-wide m6A methylation profile after METTL3 overexpression by m6A sequencing and RNA sequencing in BGC-823 cells. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed to analyze the function of core targets of METTL3. Eighteen methylation core molecules were identified in GC patients by combining transcriptome and methylome sequencing. GC patients can be separated into two subtypes based on the expression of 18 methylation core molecules. Furthermore, subgroup analysis showed that patients with different subtypes had a different OS, PFS, stage, grade, and TMB. Gene set enrichment analysis (GSEA) showed that immune-related pathways were enriched among subtype A. The ESTIMATE analysis suggested that the extent of infiltration of immune cells was different in two subtypes of GC patients. Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) database also showed that there were significant differences in the efficacy of immunotherapy among different types of GC patients. Altogether, our results reveal that METTL3-mediated m6A methylation modification is associated with the immune microenvironment and the effects of immunotherapy in GC patients. Our findings provide novel insights for clinicians in the diagnosis and optimal treatment of GC patients