UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy

In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of categories and even the unseen. Inspired by successful pipelines used in parallel gripper grasping, we split the task into two stages: 1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution. For the first stage, we propose a novel probabilistic model of grasp pose conditioned on the point cloud observation that factorizes rotation from translation and articulation. Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud.For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution. Note that it is very challenging to learn this highly generalizable grasp policy that only takes realistic inputs without oracle states. We thus propose several important innovations, including state canonicalization, object curriculum, and teacher-student distillation. Integrating the two stages, our final pipeline becomes the first to achieve universal generalization for dexterous grasping, demonstrating an average success rate of more than 60\% on thousands of object instances, which significantly outperforms all baselines, meanwhile showing only a minimal generalization gap.Comment: Accepted to CVPR 202

Combinational Scheduling Model Considering Multiple Vehicle Sizes

Urban public transport is an effective way to solve urban traffic problems and promote sustainable development of urban traffic. A scientific operation scheduling system has an important guiding significance for optimizing the configuration of urban public transport capacity resources, improving the level of operation organization and management, and providing for the sustainability of the transportation system. According to the inhomogeneous distribution of passenger flow along transit lines, this study develops a combinational scheduling model in which the enterprise supplies zonal service based on regular service. The objective function minimizes the sum of passenger travel cost and operation cost, and the simulated annealing algorithm is designed to solve the optimization model. This paper abstracts an ideal example by taking a real-world case of Bus Line 131 in Lanzhou, China. The numerical example is used to verify the validity of the model and algorithm. Results show that the combinational operation scheme can effectively satisfy passengers’ demand and reduce the total cost by 7.03% in comparison with the regular operation system. The optimal combinational system with the lowest total cost can increase the vehicle load factor and improve the utilization ratio

Comparative genomics of molybdenum utilization in prokaryotes and eukaryotes

Abstract Background Molybdenum (Mo) is an essential micronutrient for almost all biological systems, which holds key positions in several enzymes involved in carbon, nitrogen and sulfur metabolism. In general, this transition metal needs to be coordinated to a unique pterin, thus forming a prosthetic group named molybdenum cofactor (Moco) at the catalytic sites of molybdoenzymes. The biochemical functions of many molybdoenzymes have been characterized; however, comprehensive analyses of the evolution of Mo metabolism and molybdoproteomes are quite limited. Results In this study, we analyzed almost 5900 sequenced organisms to examine the occurrence of the Mo utilization trait at the levels of Mo transport system, Moco biosynthetic pathway and molybdoproteins in all three domains of life. A global map of Moco biosynthesis and molybdoproteins has been generated, which shows the most detailed understanding of Mo utilization in prokaryotes and eukaryotes so far. Our results revealed that most prokaryotes and all higher eukaryotes utilize Mo whereas many unicellular eukaryotes such as parasites and most yeasts lost the ability to use this metal. By characterizing the molybdoproteomes of all organisms, we found many new molybdoprotein-rich species, especially in bacteria. A variety of new domain fusions were detected for different molybdoprotein families, suggesting the presence of novel proteins that are functionally linked to molybdoproteins or Moco biosynthesis. Moreover, horizontal gene transfer event involving both the Moco biosynthetic pathway and molybdoproteins was identified. Finally, analysis of the relationship between environmental factors and Mo utilization showed new evolutionary trends of the Mo utilization trait. Conclusions Our data provide new insights into the evolutionary history of Mo utilization in nature

Disease Ionomics: Understanding the Role of Ions in Complex Disease

Ionomics is a novel multidisciplinary field that uses advanced techniques to investigate the composition and distribution of all minerals and trace elements in a living organism and their variations under diverse physiological and pathological conditions. It involves both high-throughput elemental profiling technologies and bioinformatic methods, providing opportunities to study the molecular mechanism underlying the metabolism, homeostasis, and cross-talk of these elements. While much effort has been made in exploring the ionomic traits relating to plant physiology and nutrition, the use of ionomics in the research of serious diseases is still in progress. In recent years, a number of ionomic studies have been carried out for a variety of complex diseases, which offer theoretical and practical insights into the etiology, early diagnosis, prognosis, and therapy of them. This review aims to give an overview of recent applications of ionomics in the study of complex diseases and discuss the latest advances and future trends in this area. Overall, disease ionomics may provide substantial information for systematic understanding of the properties of the elements and the dynamic network of elements involved in the onset and development of diseases

Structure and white LED properties of Ce-doped YAG-Al 2 O 3 eutectics grown by the micro-pulling-down method

International audienceA series of Ce-doped YAG-Al2O3 eutectics were grown by the micro-pulling-down (μ-PD) method, for the purpose of high power white light emitting diodes. The eutectic structure was investigated. A calibrated integrating sphere spectrometer setup was used to measure the white LED properties of eutectic samples. The CIE color coordinates can be regulated easily by altering the Ce3+ doping concentration and sample thickness of the Ce:YAG-Al2O3 eutectics. A 1 W white LED device based on Ce:YAG-Al2O3 eutectics at a rated current of 350 mA emitted a bright white light with a CIE coordinate of (0.319, 0.334), a luminous efficacy of 83.0 lm W−1 and a Duv value of 0.003. The results showed that Ce:YAG-Al2O3 eutectics have better luminescence properties than commercial powder phosphors at a high current density

Tracking and Reconstructing Hand Object Interactions from Point Cloud Sequences in the Wild

In this work, we tackle the challenging task of jointly tracking hand object poses and reconstructing their shapes from depth point cloud sequences in the wild, given the initial poses at frame 0. We for the first time propose a point cloud-based hand joint tracking network, HandTrackNet, to estimate the inter-frame hand joint motion. Our HandTrackNet proposes a novel hand pose canonicalization module to ease the tracking task, yielding accurate and robust hand joint tracking. Our pipeline then reconstructs the full hand via converting the predicted hand joints into a MANO hand. For object tracking, we devise a simple yet effective module that estimates the object SDF from the first frame and performs optimization-based tracking. Finally, a joint optimization step is adopted to perform joint hand and object reasoning, which alleviates the occlusion-induced ambiguity and further refines the hand pose. During training, the whole pipeline only sees purely synthetic data, which are synthesized with sufficient variations and by depth simulation for the ease of generalization. The whole pipeline is pertinent to the generalization gaps and thus directly transferable to real in-the-wild data. We evaluate our method on two real hand object interaction datasets, e.g. HO3D and DexYCB, without any fine-tuning. Our experiments demonstrate that the proposed method significantly outperforms the previous state-of-the-art depth-based hand and object pose estimation and tracking methods, running at a frame rate of 9 FPS. We have released our code on https://github.com/PKU-EPIC/HOTrack