NeBula: Team CoSTAR's robotic autonomy solution that won phase II of DARPA Subterranean Challenge

This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge. Specifically, it presents the techniques utilized within the Tunnel (2019) and Urban (2020) competitions, where CoSTAR achieved second and first place, respectively. We also discuss CoSTAR¿s demonstrations in Martian-analog surface and subsurface (lava tubes) exploration. The paper introduces our autonomy solution, referred to as NeBula (Networked Belief-aware Perceptual Autonomy). NeBula is an uncertainty-aware framework that aims at enabling resilient and modular autonomy solutions by performing reasoning and decision making in the belief space (space of probability distributions over the robot and world states). We discuss various components of the NeBula framework, including (i) geometric and semantic environment mapping, (ii) a multi-modal positioning system, (iii) traversability analysis and local planning, (iv) global motion planning and exploration behavior, (v) risk-aware mission planning, (vi) networking and decentralized reasoning, and (vii) learning-enabled adaptation. We discuss the performance of NeBula on several robot types (e.g., wheeled, legged, flying), in various environments. We discuss the specific results and lessons learned from fielding this solution in the challenging courses of the DARPA Subterranean Challenge competition.The work is partially supported by the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration (80NM0018D0004), and Defense Advanced Research Projects Agency (DARPA)

nicolas-chaulet/torch-points3d: v 1.3.0

1.3.0 Added MS-SVConv: https://arxiv.org/abs/2103.14533 (thanks @humanpose1) added new data generations techniques for the self-supervised learning (PeriodicSampling, IrregularSampling EllipsoidCrop) (thanks @humanpose1) More ETH benchmark dataset (thanks @humanpose1) Changed Minkowski 0.5 support Bug fixes Fix bug in data loader https://github.com/nicolas-chaulet/torch-points3d/issues/443 thanks @JloveU Fix bug in base unet that created problems when loading pointnet++ model checkpoin

An In Vitro Whole-Organ Liver Engineering for Testing of Genetic Therapies

Explosion of gene therapy approaches for treating rare monogenic and common liver disorders created an urgent need for disease models able to replicate human liver cellular environment. Available models lack 3D liver structure or are unable to survive in long-term culture. We aimed to generate and test a 3D culture system that allows long-term maintenance of human liver cell characteristics.The in vitro whole-organ "Bioreactor grown Artificial Liver Model" (BALM) employs a custom-designed bioreactor for long-term 3D culture of human induced pluripotent stem cells-derived hepatocyte-like cells (hiHEPs) in a mouse decellularized liver scaffold. Adeno-associated viral (AAV) and lentiviral (LV) vectors were introduced by intravascular injection.Substantial AAV and LV transgene expression in the BALM-grown hiHEPs was detected. Measurement of secreted proteins in the media allowed non-invasive monitoring of the system.We demonstrated that humanized whole-organ BALM is a valuable tool to generate pre-clinical data for investigational medicinal products